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Hardware Acceleration Market Report: Trends, Forecast and Competitive Analysis to 2031

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  • LucintelÀÇ ¿¹Ãø¿¡ µû¸£¸é, À¯Çüº°·Î´Â ±×·¡ÇÈ Ã³¸® ÀåÄ¡(GPU)°¡ ¿¹Ãø ±â°£ µ¿¾È °¡Àå ³ôÀº ¼ºÀåÀ» º¸ÀÏ °ÍÀ¸·Î Àü¸ÁÇÕ´Ï´Ù. °ÔÀÓ, ½Ã¹Ä·¹À̼Ç, µ¥ÀÌÅÍ ¼¾ÅÍ¿¡¼­ °í¼º´É ÄÄÇ»ÆÃ ¼ö¿ä Áõ°¡·Î GPU Ȱ¿ëµµ°¡ ³ô¾ÆÁö°í Àֱ⠶§¹®ÀÔ´Ï´Ù.
  • ¿ëµµº°·Î´Â µö·¯´× ÈÆ·ÃÀÌ °¡Àå ³ôÀº ¼ºÀåÀ» º¸ÀÏ °ÍÀ¸·Î ¿¹»óµË´Ï´Ù. AI ¾ÖÇø®ÄÉÀ̼ǿ¡¼­ ´õ ºü¸£°í È¿À²ÀûÀÎ ¸ðµ¨ ÈÆ·Ã¿¡ ´ëÇÑ ¼ö¿ä°¡ Áõ°¡Çϸ鼭 µö·¯´×¿¡ Çϵå¿þ¾î °¡¼Ó±â »ç¿ëÀÌ °¡¼ÓµÇ°í Àֱ⠶§¹®ÀÔ´Ï´Ù.
  • Áö¿ªº°·Î´Â ºÏ¹Ì°¡ ¿¹Ãø±â°£ Áß °¡Àå ³ôÀº ¼ºÀåÀ» ÀÌ·ê Àü¸ÁÀÔ´Ï´Ù.

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  • Çϵå¿þ¾î °¡¼Ó¿¡ AI ÅëÇÕ : AI ¿öÅ©·Îµå´Â Á¾Á¾ ¸·´ëÇÑ Ã³¸® ´É·ÂÀ» ¿ä±¸Çϸç, GPU ¹× ¸ÂÃãÇü ASIC°ú °°Àº Çϵå¿þ¾î °¡¼Ó±â´Â ¹ü¿ë ÇÁ·Î¼¼¼­º¸´Ù ÀÌ·¯ÇÑ ÀÛ¾÷À» ´õ È¿À²ÀûÀ¸·Î ó¸®Çϵµ·Ï ¼³°èµÇ¾ú½À´Ï´Ù. AI ±â¹Ý Çϵå¿þ¾î °¡¼Ó±â´Â ¸Ó½Å ·¯´×, µö ·¯´×, µ¥ÀÌÅÍ ºÐ¼®°ú °°Àº ¾ÖÇø®ÄÉÀ̼ǿ¡¼­ ÇÙ½ÉÀûÀÎ ¿ªÇÒÀ» Çϰí ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ ÅëÇÕÀº ÀÇ·á, ÀÚµ¿Â÷, ±ÝÀ¶°ú °°Àº »ê¾÷¿¡¼­ ¼º´ÉÀ» Çâ»ó½ÃÄÑ ´õ ºü¸¥ ÀÇ»ç °áÁ¤°ú ½Ç½Ã°£ ÀλçÀÌÆ®¸¦ °¡´ÉÇÏ°Ô ÇÕ´Ï´Ù.
  • FPGA Ȱ¿ë : ³ôÀº ÀûÀÀ¼ºÀ» Áö´Ñ FPGA´Â Çϵå¿þ¾î °¡¼Ó ½ÃÀå¿¡¼­ Ȱ¿ëµË´Ï´Ù. FPGA´Â ƯÁ¤ ¾ÖÇø®ÄÉÀ̼ǿ¡ µû¶ó ÀçÇÁ·Î±×·¡¹ÖÀÌ °¡´ÉÇÕ´Ï´Ù. FPGA´Â ³ôÀº Ä¿½ºÅ͸¶ÀÌ¡ÀÌ °¡´ÉÇϸç, Åë½Å, µ¥ÀÌÅÍ ¼¾ÅÍ, ÀÚµ¿Â÷ »ê¾÷¿¡¼­ ÀúÁö¿¬ ¹× °í󸮷® ´Þ¼º¿¡ ÀûÇÕÇÕ´Ï´Ù. À̹ÌÁö 󸮳ª ºñµð¿À ÀÎÄÚµù°ú °°Àº ƯÁ¤ ÀÛ¾÷À» À§ÇÑ Àü¿ë Çϵå¿þ¾î °¡¼ÓÀÌ ÇÊ¿äÇÑ »ê¾÷¿¡ FPGA´Â ¸Å¿ì Áß¿äÇÕ´Ï´Ù. ÀÌ·¯ÇÑ À¯¿¬¼º°ú ÀÛ¾÷ ÃÖÀûÈ­ ´É·ÂÀº °í¼º´É ÄÄÇ»ÆÃ ȯ°æ¿¡¼­ FPGA »ç¿ë Áõ°¡¸¦ ÁÖµµÇϰí ÀÖ½À´Ï´Ù.
  • ¿§Áö ÄÄÇ»ÆÃ°ú Çϵå¿þ¾î °¡¼ÓÀÇ ºÎ»ó : ¿§Áö ÄÄÇ»ÆÃÀº ³×Æ®¿öÅ© °¡ÀåÀÚ¸®¿¡¼­ Çϵå¿þ¾î °¡¼Ó¿¡ ´ëÇÑ °­·ÂÇÑ ¼ö¿ä¸¦ âÃâÇß½À´Ï´Ù. ¿§Áö µð¹ÙÀ̽º´Â Áö¿¬ ½Ã°£À» ÇÇÇϱâ À§ÇØ µ¥ÀÌÅ͸¦ ·ÎÄÿ¡¼­ ½Ç½Ã°£À¸·Î ó¸®ÇØ¾ß ÇÏ´Â °æ¿ì°¡ ¸¹À¸¹Ç·Î GPU ¹× AI Ĩ°ú °°Àº Çϵå¿þ¾î °¡¼Ó±â°¡ ÇʼöÀûÀÔ´Ï´Ù. ÀÚÀ² ÁÖÇà, ½º¸¶Æ® ½ÃƼ, IoT¿Í °°ÀÌ µ¥ÀÌÅÍÀÇ ½Ç½Ã°£ 󸮰¡ Áß¿äÇÑ »ê¾÷¿¡¼­ ¿§Áö ÄÄÇ»ÆÃÀÇ µµÀÔÀÌ ÁøÇà ÁßÀÔ´Ï´Ù. ÀÌ·¯ÇÑ Ãß¼¼ÀÇ ¼ºÀåÀº ¿¡Áö¿¡¼­ AI ¹× ¸Ó½Å·¯´× ¸ðµ¨À» Áö¿øÇϱâ À§ÇÑ Çϵå¿þ¾î °¡¼Ó Çʿ伺¿¡ ÀÇÇØ ÁÖµµµÇ°í ÀÖ½À´Ï´Ù.
  • ¾çÀÚ ÄÄÇ»ÆÃ°ú Çϵå¿þ¾î °¡¼Ó : »õ·Î¿î ÆÐ·¯´ÙÀÓÀº ¾çÀÚ ÄÄÇ»ÆÃÀÇ ºÎ»óÀÔ´Ï´Ù. ¾çÀÚ ÄÄÇ»ÆÃÀº Çϵå¿þ¾î °¡¼Ó ºÐ¾ß¿¡¼­ µîÀåÇÑ »õ·Î¿î Æ®·»µå Áß ÇϳªÀÓÀÌ ºÐ¸íÇÕ´Ï´Ù. Ãʱ⠴ܰ迡 ÀÖ´Â ¾çÀÚ ÄÄÇ»ÆÃÀº µ¥ÀÌÅÍ Ã³¸®ÀÇ ÁöÇüÀ» ¹Ù²Ü °ÍÀ» ¾à¼ÓÇÕ´Ï´Ù ¹× ÀüÅëÀûÀÎ ÀÌÁø ºñÆ®(bit) ´ë ¾çÀÚ ºñÆ®(qubit)ÀÇ ´ë°áÀÔ´Ï´Ù. ÃÖ»óÀÇ Çϵå¿þ¾î °¡¼Ó±â´Â ¾çÀÚ ÄÄÇ»ÆÃÀÇ Çâ»óµÈ °è»ê ¼Óµµ¸¦ Ȱ¿ëÇϱâ À§ÇÑ ÃÖÀûÈ­¸¦ ¸ñÇ¥·Î ÇÕ´Ï´Ù. ÀÌ ±â¼úÀº ¾ÏÈ£ÇÐ, Àç·á °úÇÐ, º¹ÀâÇÑ ½Ã¹Ä·¹ÀÌ¼Ç µîÀÇ ºÐ¾ß¿¡ Àû¿ëµÉ °¡´É¼ºÀÌ ³ô½À´Ï´Ù. ¾çÀÚ ÄÄÇ»ÆÃÀÌ ¹ßÀüÇÔ¿¡ µû¶ó Çϵå¿þ¾î °¡¼Ó±â´Â ÀÌ ±â¼úÀ» ½Ç¿ëÈ­ ´Ü°è·Î ÇÑ °ÉÀ½ ´õ °¡±îÀÌ À̲ø Áß¿äÇÑ ÃËÁøÁ¦°¡ µÉ °ÍÀÔ´Ï´Ù.
  • ºí·ÏüÀÎ ±â¼ú¿¡¼­ Çϵå¿þ¾î °¡¼Ó±â Àû¿ë : ºí·ÏüÀÎ ±â¼úÀº °Å·¡ ó¸® ¹× °ËÁõ °¡¼Ó¸¦ À§ÇØ Çϵå¿þ¾î °¡¼Ó±â¸¦ Ȱ¿ëÇϱ⠽ÃÀÛÇß½À´Ï´Ù. ¾ÏȣȭÆó ¹× Å»Áß¾ÓÈ­ ±ÝÀ¶(DeFi) Æ®·»µå°¡ ºÎ»óÇÔ¿¡ µû¶ó È¿À²ÀûÀÎ ºí·ÏüÀÎ ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ö¿ä°¡ Å©°Ô Áõ°¡Çϰí ÀÖ½À´Ï´Ù. ASIC ¹× FPGA´Â ºí·ÏüÀÎ ³×Æ®¿öÅ© ¼º´É Çâ»ó, 󸮷® Áõ°¡, Áö¿¬ ½Ã°£ °¨¼Ò ¸ñÀûÀ¸·Î Çϵå¿þ¾î °¡¼Ó±â¿¡ Àû¿ëµË´Ï´Ù. À̸¦ ÅëÇØ ä±¼ÀÚ°¡ ÃÊ´ç ´õ ¸¹Àº °Å·¡¸¦ ó¸®ÇÒ ¼ö ÀÖ°Ô µÇ¾î ºí·ÏüÀÎ ¾ÖÇø®ÄÉÀ̼ÇÀÇ È¿À²¼º°ú È®À强ÀÌ Çâ»óµÉ °ÍÀÔ´Ï´Ù. ÀÌ·¯ÇÑ Ãß¼¼´Â ƯÈ÷ ±ÝÀ¶ ¹× °ø±Þ¸Á °ü¸® »ê¾÷¿¡¼­ Áß¿äÇÕ´Ï´Ù.

AI ÅëÇÕ, ¿§Áö ÄÄÇ»ÆÃ, FPGA µµÀÔ, ¾çÀÚ ÄÄÇ»ÆÃ, ºí·ÏüÀÎ ¾ÖÇø®ÄÉÀÌ¼Ç µî Çϵå¿þ¾î °¡¼Ó ½ÃÀåÀÇ ½ÅÈï Æ®·»µå´Â »ê¾÷ÀÇ ¸ð½ÀÀ» º¯È­½Ã۰í ÀÖÀ¸¸ç, ´õ ºü¸£°í È¿À²ÀûÀΠó¸® ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ö¿ä¿¡ ¿µÇâÀ» ¹ÌÄ¡°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ »õ·Î¿î ¾ÖÇø®ÄÉÀ̼ÇÀÌ °è¼Ó ¹ßÀüÇÔ¿¡ µû¶ó Çϵå¿þ¾î °¡¼Ó±â´Â ¿©ÀüÈ÷ ÇÙ½É ¿ªÇÒÀ» ¼öÇàÇÒ °ÍÀÔ´Ï´Ù.

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Çϵå¿þ¾î °¡¼Ó ½ÃÀåÀº °í¼º´É ȯ°æ¿¡¼­ µ¥ÀÌÅÍ Ã³¸® ¹æ½ÄÀ» º¯È­½ÃŰ´Â ¿©·¯ ÁÖ¿ä ¹ßÀüÀ» ¸ñ°ÝÇØ ¿Ô½À´Ï´Ù. ÀÌ·¯ÇÑ ¹ßÀüÀº Çϵå¿þ¾î ¾ÆÅ°ÅØÃ³ÀÇ Áøº¸, ½Å±â¼ú°úÀÇ ÅëÇÕ, ±×¸®°í ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ ¸ÂÃãÇü ¼Ö·ç¼Ç¿¡ ´ëÇÑ ¼ö¿ä Áõ°¡¸¦ ¹Ý¿µÇÕ´Ï´Ù.

  • GPU ±â¼ú ¹ßÀü : GPU´Â Çϵå¿þ¾î °¡¼ÓÀÇ ÃÖ÷´Ü¿¡ ÀÖÀ¸¸ç, ÃÖ±Ù ¹ßÀüÀ¸·Î ÀÌ·¯ÇÑ ÇÁ·Î¼¼¼­ÀÇ ¼º´É°ú È¿À²¼ºÀÌ È¹±âÀûÀ¸·Î Çâ»óµÇ¾ú½À´Ï´Ù. NVIDIA¿Í AMD¿Í °°Àº ±â¾÷µéÀº ´õ ºü¸¥ ó¸® ¼Óµµ, Çâ»óµÈ ¸Þ¸ð¸®, ±×¸®°í ´õ ³ôÀº ¿¡³ÊÁö È¿À²¼ºÀ» °®Ãá Â÷¼¼´ë GPU¸¦ »ý»êÇØ ¿Ô½À´Ï´Ù. ÀÌ´Â ½Ç½Ã°£À¸·Î µ¥ÀÌÅ͸¦ ó¸®ÇÏ°í ºÐ¼®Çϱâ À§ÇØ ³ôÀº ¿¬»ê ´É·ÂÀÌ ¿ä±¸µÇ´Â °ÔÀÓ, AI, ºòµ¥ÀÌÅÍ ºÐ¼® ºÐ¾ßÀÇ ¾ÖÇø®ÄÉÀ̼ǿ¡ ¸Å¿ì Áß¿äÇÕ´Ï´Ù.
  • AI Àü¿ë Çϵå¿þ¾î °¡¼Ó±â : ¸Ó½Å·¯´× ¹× µö·¯´× ¾Ë°í¸®Áò »ç¿ëÀÌ Áõ°¡ÇÔ¿¡ µû¶ó À̵éÀº ºñ¾àÀûÀ¸·Î ¼ºÀåÇØ ¿Ô½À´Ï´Ù. ±¸±ÛÀÇ TPU¿Í ÀÎÅÚÀÇ Nervana Ĩ°ú °°Àº ±â¾÷µéÀº AI ¿öÅ©·Îµå¸¦ ±â¹ÝÀ¸·Î ÇÑ Àü¿ë Çϵå¿þ¾î¸¦ ±¸ÃàÇÕ´Ï´Ù. ÀÌ·¯ÇÑ °¡¼Ó±â´Â ÀÚ¿¬¾î ó¸®, À̹ÌÁö ÀνÄ, ÀÚÀ² ÁÖÇà°ú °°Àº ÀÛ¾÷ÀÇ ¼º´ÉÀ» ÃÖÀûÈ­Çϵµ·Ï ¼³°èµÇ¾ú½À´Ï´Ù. À̵éÀº °è»ê ¼Óµµ¸¦ Çâ»ó½ÃŰ°í ¿¡³ÊÁö ¼Òºñ¸¦ ÁÙÀÔ´Ï´Ù.
  • µ¥ÀÌÅÍ ¼¾ÅÍ¿¡¼­ FPGA ±â¼ú : FPGA´Â Àü¿ë ÀÛ¾÷À» À§ÇØ µ¥ÀÌÅÍ ¼¾ÅÍ¿¡ Á¡Á¡ ´õ ¸¹ÀÌ Æ÷ÇԵǰí ÀÖ½À´Ï´Ù. FPGA´Â À¯¿¬¼º°ú ³·Àº Áö¿¬ ½Ã°£À» Á¦°øÇÏ¿© ¾Ïȣȭ, ½Ç½Ã°£ ºÐ¼®, °íºóµµ °Å·¡ µî ¸ÂÃãÇü 󸮰¡ ÇÊ¿äÇÑ ¾ÖÇø®ÄÉÀ̼ǿ¡ ÀûÇÕÇÕ´Ï´Ù. ´Ù¾çÇÑ ¿öÅ©·Îµå¿¡ ¸Â°Ô ÀçÇÁ·Î±×·¡¹ÖÀÌ °¡´ÉÇØ È¿À²¼º°ú È®À强ÀÌ ÃÖ¿ì¼±ÀÎ µ¥ÀÌÅÍ ¼¾ÅÍ¿¡¼­ ¼±È£µÇ´Â ¼±ÅÃÁöÀÔ´Ï´Ù. ±â¾÷µéÀº µ¥ÀÌÅÍ ¼¾ÅÍ ¼º´É Çâ»ó°ú ¿î¿µ ºñ¿ë Àý°¨À» À§ÇØ FPGA ¼Ö·ç¼Ç¿¡ ´ë±Ô¸ð ÅõÀÚ¸¦ ÁøÇà ÁßÀÔ´Ï´Ù.
  • Ư¼ö ¸ñÀû ASIC ĨÀÇ ºÎ»ó : ¾ÖÇø®ÄÉÀÌ¼Ç Àü¿ë ÁýÀû ȸ·Î(ASIC)´Â Çϵå¿þ¾î °¡¼Ó ºÐ¾ß¿¡¼­, ƯÈ÷ ºí·ÏüÀÎ ¹× ¾ÏȣȭÆó ä±¼°ú °°Àº ¿µ¿ª¿¡¼­ ´õ¿í µÎµå·¯Áö°Ô ºÎ»óÇϰí ÀÖ½À´Ï´Ù. ASICÀº ¹ü¿ë ÇÁ·Î¼¼¼­º¸´Ù ´ÜÀÏ ÀÛ¾÷À» ´õ È¿À²ÀûÀ¸·Î ¼öÇàÇϵµ·Ï Á¦ÀÛµÈ ¸ÂÃãÇü ĨÀÔ´Ï´Ù. ÀÌ·¯ÇÑ ¸ÂÃãÇü ĨÀº ¾ÏȣȭÆó ä±¼À̳ª ºñµð¿À ÀÎÄÚµù°ú °°ÀÌ ¹Ýº¹ÀûÀÎ °è»êÀÌ ÇÊ¿äÇÑ ¾ÖÇø®ÄÉÀ̼ǿ¡ Ź¿ùÇÕ´Ï´Ù. Àü·Â ¼Òºñ¿Í ó¸® ¼Óµµ Ãø¸é¿¡¼­ È¿À²¼ºÀº ±ÝÀ¶ ¹× Åë½Å°ú °°Àº ´õ ¸¹Àº »ê¾÷ ºÐ¾ß¿¡¼­ Ȱ¿ëÀ» ÃËÁøÇÒ °ÍÀÔ´Ï´Ù.
  • ÀÚµ¿Â÷ ½Ã½ºÅÛ ³» Çϵå¿þ¾î °¡¼Ó±â ¼ºÀå : ÀÚµ¿Â÷ ±â¾÷µéÀº ÀÚÀ²ÁÖÇà Â÷·® ½Ã½ºÅÛ¿¡ ´õ ¸¹Àº Çϵå¿þ¾î °¡¼Ó±â¸¦ µµÀÔÇϰí ÀÖ½À´Ï´Ù. GPU ¹× AI ĨÀ» Æ÷ÇÔÇÑ Çϵå¿þ¾î °¡¼Ó±â´Â ÀÚÀ²ÁÖÇà Â÷·® ³» ½Ç½Ã°£ ¹°Ã¼ °¨Áö, ³»ºñ°ÔÀ̼Ç, ÀÇ»ç°áÁ¤¿¡ ÇÊ¿äÇÑ º¹ÀâÇÑ ¾Ë°í¸®ÁòÀ» ±¸µ¿ÇÕ´Ï´Ù. ÀÚµ¿Â÷ »ê¾÷ÀÇ ÀÚÀ²ÁÖÇà ¹× ¿îÀüÀÚ º¸Á¶ ½Ã½ºÅÛ¿¡ ´ëÇÑ °ü½ÉÀÌ Áõ°¡ÇÔ¿¡ µû¶ó, ¼¾¼­¿Í Ä«¸Þ¶ó¿¡¼­ ¹ß»ýÇÏ´Â ´ë·®ÀÇ µ¥ÀÌÅ͸¦ ó¸®Çϱâ À§ÇÑ Çϵå¿þ¾î °¡¼Ó±â´Â Çʼö ¿ä¼Ò°¡ µÇ°í ÀÖ½À´Ï´Ù. ÀÌ·¯ÇÑ Ãß¼¼´Â ÇâÈÄ ¸î ³â°£ Çϵå¿þ¾î °¡¼Ó ½ÃÀåÀÇ »ó´çÇÑ ¼ºÀåÀ» À̲ø °ÍÀ¸·Î ¿¹»óµË´Ï´Ù.

GPU, AI Àü¿ë Çϵå¿þ¾î, FPGA, ASIC ¹× ÀÚµ¿Â÷ ½Ã½ºÅÛÀÇ ¹ßÀü°ú ÇÔ²² Çϵå¿þ¾î °¡¼ÓÀÇ »õ·Î¿î Æ®·»µå´Â ´Ù¾çÇÑ ºÐ¾ß¿¡¼­ Çõ½Å ÀáÀç·ÂÀ» °¡Áú °ÍÀÔ´Ï´Ù. ÀÌ´Â µ¥ÀÌÅÍ Ã³¸® ¼Óµµ¸¦ ³ôÀÌ°í ¼º´É ¹× ¿î¿µ È¿À²¼ºÀ» Çâ»ó½ÃÄÑ ´Ù¾çÇÑ ¾ÖÇø®ÄÉÀ̼ǿ¡¼­ ´õ »õ·Ó°í, Áøº¸ÀûÀ̸ç, È¿À²ÀûÀÎ ½Ã½ºÅÛÀ¸·ÎÀÇ ¹®À» ¿­¾îÁÖ°í ÀÖ½À´Ï´Ù.

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  • Advanced Micro Devices
  • Intel Corporation
  • Lenovo Group
  • Nvidia Corporation
  • IBM Corporation
  • Xilinx
  • Oracle Corporation

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HBR

The future of the global hardware acceleration market looks promising with opportunities in the deep learning training, public cloud inference, and enterprise cloud inference markets. The global hardware acceleration market is expected to grow with a CAGR of 49.1% from 2025 to 2031. The major drivers for this market are the growing demand for high-performance computing and the advancements in cloud computing & data centers.

  • Lucintel forecasts that, within the type category, graphics processing units are expected to witness the highest growth over the forecast period due to increasing demand for high-performance computing in gaming, simulations, and data centers, which is boosting GPU utilization.
  • Within the application category, deep learning training is expected to witness the highest growth due to the growing demand for faster and more efficient model training in AI applications, which is accelerating the use of hardware accelerators in deep learning.
  • In terms of region, North America is expected to witness the highest growth over the forecast period.

Emerging Trends in the Hardware Acceleration Market

Hardware acceleration market emerging trends are changing the game of different industries worldwide as it evolves today. These changes in trends show how processing, efficiency, and adaptability related to hardware accelerators are being improved while heightened reliance on applications related to AI and big data.

  • The integration of AI into hardware acceleration: AI workloads often require massive processing power, and hardware accelerators like GPUs and custom ASICs are designed to handle these tasks more efficiently than general-purpose processors. AI-driven hardware accelerators are becoming crucial in applications such as machine learning, deep learning, and data analytics. This integration improves performance in industries such as healthcare, automotive, and finance, allowing for faster decision-making and real-time insights.
  • Utilization of FPGAs: With the usage of FPGAs that are highly adaptable, they are utilized in the hardware acceleration market. FPGAs can be reprogrammed according to specific applications. FPGAs are highly customizable and are suitable for applications like telecommunications, data centers, and automotive with respect to achieving low latency and high throughput. FPGAs are of great importance to those industries that have specific tasks, such as image processing or video encoding, for dedicated hardware acceleration. Their flexibility and ability to optimize tasks are driving their increasing use in high-performance computing environments.
  • Rise of Edge Computing and Hardware Acceleration: Edge computing has given rise to a strong demand for hardware acceleration at the network's edge. Edge devices often need to process data locally and in real-time to avoid latency, so hardware accelerators such as GPUs and AI chips are essential. The adoption of edge computing is taking place in industries such as autonomous driving, smart cities, and IoT, where real-time processing of data is critical. The growth in this trend is being driven by the need for hardware acceleration to support AI and machine learning models at the edge.
  • Quantum Computing and Hardware Acceleration: The new paradigm is the rise of quantum computing. No doubt, quantum computing is one of the new trends that emerged in hardware acceleration. In its infancy, quantum computing promises to change the landscape of processing data-traditional binary bits versus the quantum bit, or qubits. The best hardware accelerators aim for optimization to leverage enhanced calculation speeds into quantum computing. This technology is likely to be applied in areas such as cryptography, material science, and complex simulations. As quantum computing advances, hardware accelerators are going to become an important enabler of this technology to bring it closer to being used in practice.
  • Application of Hardware Accelerators in Blockchain Technology: Blockchain technology has started using hardware acceleration for accelerating the processing and verification of transactions. As the cryptocurrency and decentralized finance (DeFi) trend rises, there is a great demand for efficient blockchain solutions. ASICs and FPGAs are applied in hardware accelerators to boost the performance of blockchain networks, increase throughput, and decrease latency. With this, the efficiency and scalability of blockchain applications will be increased due to miners' ability to process more transactions per second. This trend is particularly important in the finance and supply chain management industries.

Emerging trends in the hardware acceleration market, like AI integration, edge computing, FPGA adoption, quantum computing, and blockchain applications, are all changing the face of industries and are impacting demand for faster and more efficient processing solutions. As these new applications continue to develop, hardware accelerators will remain at the epicenter of the action.

Recent Developments in the Hardware Acceleration Market

The hardware acceleration market has witnessed several key developments that are transforming the way data is processed in high-performance environments. These developments reflect advances in hardware architecture, integration with emerging technologies, and the growing demand for custom solutions across various sectors.

  • Advancements in GPU Technology: GPUs are at the cutting edge of hardware acceleration, and the latest developments have dramatically improved the performance and efficiency of these processors. Companies such as NVIDIA and AMD have produced newer generations of GPUs with faster processing, improved memory, and greater energy efficiency. All this is critical for applications in gaming, AI, and big data analytics, where high computational power is required to process and analyze data in real-time.
  • AI-Specific Hardware Accelerators: They have grown in leaps and bounds since the increasing growth of machine and deep learning algorithm usage. Companies such as Google with its TPU and Intel with its Nervana chips build specialized hardware based on AI workloads. These accelerators are designed to optimize the performance of tasks like natural language processing, image recognition, and autonomous driving. They improve computation speed and decrease energy consumption.
  • FPGA Technology in Data Centers: FPGAs are being increasingly included in data centers for dedicated tasks. FPGAs provide flexibility and low latency, which are suitable for applications that need customized processing, such as encryption, real-time analytics, and high-frequency trading. They can be reprogrammed for different workloads, making them a popular choice for data centers, where efficiency and scalability are paramount. Companies are investing heavily in FPGA solutions to improve the performance of data centers and reduce their operating costs.
  • Rise of Specialized ASIC Chips: Application-specific integrated circuits (ASICs) have become more prominent in hardware acceleration, especially in areas such as blockchain and cryptocurrency mining. Custom chips made to perform a single task more efficiently than a general-purpose processor are ASICs. Such custom chips are great for applications requiring repetitive computations, such as cryptocurrency mining or video encoding. Their efficiency in power consumption and processing speed will also encourage their use in more industries, such as finance and telecommunications.
  • Growth of Hardware Accelerators in Automotive Systems: Automotive companies are adopting more hardware accelerators in autonomous vehicle systems. Hardware accelerators, including GPUs and AI chips, power complex algorithms necessary for the real-time detection of objects, navigation, and decisions inside an autonomous vehicle. With the growing interest of the automotive industry in autonomous driving and driver assistance systems, hardware accelerators are becoming a critical necessity for processing large volumes of data from sensors and cameras. This trend is expected to drive significant growth in the hardware acceleration market in the coming years.

New trends in hardware acceleration with advances in GPUs, AI-specific hardware, FPGA, ASIC, and automotive systems will have an innovation potential across different sectors. This is bringing a faster pace to data processing and better performance as well as operational efficiency, thereby opening the gateways for newer, advanced, and more efficient systems in various applications.

Strategic Growth Opportunities in the Hardware Acceleration Market

The hardware acceleration market holds immense growth opportunities across key applications due to advancements in processing technologies and the need for high-performance computing. All these opportunities are setting up the future for industries like AI, automotive, finance, and telecommunication.

  • AI and Machine Learning Applications: The growing demand for AI and machine learning applications presents a significant growth opportunity for hardware accelerators. AI algorithms require significant computational power, and hardware accelerators like GPUs, TPUs, and FPGAs are designed to meet these needs. As AI adoption increases across industries like healthcare, automotive, and finance, the need for more efficient and specialized hardware accelerators will continue to rise.
  • Edge Computing and IoT: Edge computing is increasingly becoming a fundamental component of the hardware acceleration market, especially as the Internet of Things (IoT) continues to grow. Edge devices should process data locally to reduce latency and enhance real-time decision-making. Hardware accelerators, such as AI chips and GPUs, are really important for allowing faster data processing at the edge. These have made low-latency processing essential to applications such as autonomous vehicles, smart cities, and industrial IoT.
  • Blockchain and Cryptocurrency: Blockchain technology is a major market driver for the hardware acceleration business, especially mining cryptocurrencies. It requires specialized hardware, such as ASICs and FPGAs, to process transactions and mine cryptocurrencies efficiently. As blockchain technology expands into other sectors in finance and supply chain management, the demand for these hardware accelerators will grow. This opportunity will allow firms to develop solutions that optimize blockchain performance for marketing.
  • High-Performance Computing (HPC): Rising applications of HPC are also a key growth opportunity. As industries such as research, simulation, and analytics of complex data require HPC, hardware accelerators like GPUs, TPUs, and FPGAs can have a huge impact in speeding up computations. The increasing necessity for faster, more efficient computational capability is driving hardware accelerators in the data centers of research institutions and academic environments.
  • Automotive and Autonomous Vehicles: The automotive sector, especially autonomous driving, presents tremendous growth potential for hardware accelerators. Processing real-time sensor and camera data in self-driving cars requires high-performance hardware accelerators to fuel algorithms for object detection, navigation, and decision-making. As the automotive industry continues to innovate in autonomous driving and ADAS (Advanced Driver Assistance Systems), so will the demand for hardware accelerators.

Future opportunities in AI, edge computing, blockchain, HPC, and automotive systems are driving strategic growth in the hardware acceleration market. These areas present a picture where hardware accelerators play an indispensable role in applications involving performance improvement, scalability, and efficiency.

Hardware Acceleration Market Driver and Challenges

Technological advancement, economic pressure, and regulatory considerations are factors that influence the hardware acceleration market. These drivers and challenges affect the adoption and growth of hardware accelerators across industries, thereby determining the direction of market trends and innovation.

The factors responsible for driving the hardware acceleration market include:

1. Technological advancements: Hardware acceleration is driven by the growth of processing technologies, such as GPUs, FPGAs, and ASICs. In fact, these innovations allow complex workloads to be processed much faster and more efficiently. Therefore, hardware accelerators play a role in applications relating to AI, machine learning, and high-performance computing.

2. Rising demand for AI and big data: The increased use of AI, machine learning, and big data analytics is one of the biggest drivers for hardware accelerators. These technologies require a huge amount of computational power, and hardware accelerators provide the necessary speed and efficiency to handle large datasets and complex algorithms.

3. Cloud computing and data center boom: As the boom of cloud computing and data centers continues, so does the rise in demand for hardware accelerators. Data centers are using high-performance hardware for AI, big data, and virtualization technologies, further increasing the requirements with the boom in cloud.

4. Edge computing and IoT growth: Edge computing and the Internet of Things, in general, are creating increased demand for hardware accelerators located at the network's edge. Low-latency processing and real-time decision-making require specialist hardware to undertake local computations that will be creating new growth opportunities in hardware acceleration technology.

5. Advances in automotive technologies: The use of autonomous vehicles and other automotive-related technologies is advancing the use of hardware accelerators because these systems must operate with real-time processing to ensure both safety and efficiency.

Challenges in the hardware acceleration market are:

1. High cost of specialized hardware: Among the main challenges in the hardware acceleration market is the high cost associated with specialized hardware. These include high-performance chips such as GPUs, FPGAs, and ASICs, which are very expensive to design and deploy. Access is thereby limited to small businesses and startups.

2. Complex integration with existing systems: Integrating hardware accelerators into existing infrastructures can be complex and costly. Many companies face challenges in ensuring compatibility between new hardware and legacy systems, which can slow down adoption and reduce overall efficiency.

3. Privacy and data security concerns: As hardware accelerators handle large volumes of sensitive data, privacy and data security concerns are significant challenges. Companies must ensure that their systems comply with data protection regulations and securely handle user data to avoid security breaches.

The growing demand for AI and big data, technological advancement, and the growth of cloud computing, edge computing, and automotive technologies drive the hardware acceleration market. But high costs, integration complexities, and data security issues need to be overcome to maintain growth and acceptance of hardware accelerators.

List of Hardware Acceleration Companies

Companies in the market compete on the basis of product quality offered. Major players in this market focus on expanding their manufacturing facilities, R&D investments, infrastructural development, and leveraging integration opportunities across the value chain. With these strategies, hardware acceleration companies cater to increasing demand, ensure competitive effectiveness, develop innovative products & technologies, reduce production costs, and expand their customer base. Some of the hardware acceleration companies profiled in this report include:

  • Advanced Micro Devices
  • Intel Corporation
  • Lenovo Group
  • Nvidia Corporation
  • IBM Corporation
  • Xilinx
  • Oracle Corporation

Hardware Acceleration Market by Segment

The study includes a forecast for the global hardware acceleration market by type, application, and region.

Hardware Acceleration Market by Type [Value from 2019 to 2031]:

  • Graphics Processing Unit
  • Video Processing Unit
  • AI Accelerator
  • Regular Expression Accelerator
  • Cryptographic Accelerator
  • Others

Hardware Acceleration Market by Application [Value from 2019 to 2031]:

  • Deep Learning Training
  • Public Cloud Inference
  • Enterprise Cloud Inference
  • Others

Hardware Acceleration Market by Region [Value from 2019 to 2031]:

  • North America
  • Europe
  • Asia Pacific
  • The Rest of the World

Country Wise Outlook for the Hardware Acceleration Market

The hardware acceleration market is growing rapidly as industries around the world embrace innovations that enhance processing speeds and reduce computational workloads. Hardware acceleration is the use of specialized hardware components to perform certain functions more efficiently than software alone. In markets such as the United States, China, Germany, India, and Japan, the adoption of hardware acceleration solutions is being driven by advancements in AI, machine learning, and high-performance computing. This technology is particularly critical in applications such as data centers, gaming, automotive, and cloud computing, where performance and scalability are critical.

  • United States: The hardware acceleration market in the United States is growing rapidly due to advancements in AI and data center technologies. Major players like NVIDIA and Intel are leading the market with GPU and FPGA solutions that improve processing efficiency and speed. The growing use of cloud computing and the rise in the demand for AI-based applications have dramatically increased the demand for hardware acceleration. Another reason is that U.S. companies are highly investing in research and development in hardware accelerators to increase their performance to efficiently support big data analytics and real-time decision-making systems.
  • China: The hardware acceleration market in China is booming with the pace of technological advancements and increasing focus on artificial intelligence (AI) and 5G networks. Among those companies engaged in the development of customized hardware accelerators, such as AI chips designed for deep learning applications, Baidu and Huawei are at the forefront in China. The Chinese government has also been prompting advancements in high-performance computing (HPC) and AI-driven technologies; thus, growing the demand for hardware acceleration solutions in industries such as autonomous vehicles, healthcare, and manufacturing. This country is likely to expand this market since it aims to be a world leader in AI.
  • Germany: The hardware acceleration market in Germany is expanding because of the growing demand for high-performance computing (HPC) and data analytics solutions. The automotive, healthcare, and manufacturing sectors have been the highest adopters of hardware acceleration technology. Companies such as SAP are developing AI-driven hardware solutions to enable data processing capabilities to speed up and enhance overall operations. There is also growing support from Germany toward Industry 4.0 and further digital transformation processes in manufacturing industries. The use of hardware accelerators, such as GPUs and FPGAs, is increasing efficiency for complex simulations and real-time processing in these industries.
  • India: The use of hardware acceleration technology in the country is on an uptrend, with the country's IT and telecommunications markets growing multifold. Improvements in AI, big data, and cloud computing infrastructure being pursued by the Indian government have increased the demand for specialized hardware. Companies in India are integrating AI, machine learning, and high-performance computing solutions into their operations, and the use of hardware accelerators is becoming critical for optimizing these applications. In the semiconductor industry, startups are also contributing to the development of custom hardware accelerators, which enhance performance for domestic and international clients.
  • Japan: Hardware acceleration is being adopted across several sectors, including automotive, robotics, and electronics, in Japan. Japanese companies like Sony and Toyota are integrating hardware acceleration solutions into their research and development of AI-powered robotics and autonomous vehicles. The growing demand for real-time data processing in industries like healthcare and manufacturing is driving the expansion of hardware acceleration technologies, including GPUs and FPGAs. Japan's focus on technological innovation, particularly in AI and robotics, is propelling the demand for hardware accelerators to improve processing efficiency and enable more advanced machine learning algorithms.

Features of the Global Hardware Acceleration Market

  • Market Size Estimates: Hardware acceleration market size estimation in terms of value ($B).
  • Trend and Forecast Analysis: Market trends (2019 to 2024) and forecast (2025 to 2031) by various segments and regions.
  • Segmentation Analysis: Hardware acceleration market size by type, application, and region in terms of value ($B).
  • Regional Analysis: Hardware acceleration market breakdown by North America, Europe, Asia Pacific, and Rest of the World.
  • Growth Opportunities: Analysis of growth opportunities in different types, applications, and regions for the hardware acceleration market.
  • Strategic Analysis: This includes M&A, new product development, and the competitive landscape of the hardware acceleration market.

Analysis of the competitive intensity of the industry based on Porter's Five Forces model.

This report answers the following 11 key questions:

  • Q.1. What are some of the most promising, high-growth opportunities for the hardware acceleration market by type (graphics processing unit, video processing unit, AI accelerator, regular expression accelerator, cryptographic accelerator, and others), application (deep learning training, public cloud inference, enterprise cloud inference, and others), and region (North America, Europe, Asia Pacific, and the Rest of the World)?
  • Q.2. Which segments will grow at a faster pace and why?
  • Q.3. Which region will grow at a faster pace and why?
  • Q.4. What are the key factors affecting market dynamics? What are the key challenges and business risks in this market?
  • Q.5. What are the business risks and competitive threats in this market?
  • Q.6. What are the emerging trends in this market and the reasons behind them?
  • Q.7. What are some of the changing demands of customers in the market?
  • Q.8. What are the new developments in the market? Which companies are leading these developments?
  • Q.9. Who are the major players in this market? What strategic initiatives are key players pursuing for business growth?
  • Q.10. What are some of the competing products in this market and how big of a threat do they pose for loss of market share by material or product substitution?
  • Q.11. What M&A activity has occurred in the last 5 years and what has its impact been on the industry?

Table of Contents

1. Executive Summary

2. Market Overview

  • 2.1 Background and Classifications
  • 2.2 Supply Chain

3. Market Trends & Forecast Analysis

  • 3.1 Macroeconomic Trends and Forecasts
  • 3.2 Industry Drivers and Challenges
  • 3.3 PESTLE Analysis
  • 3.4 Patent Analysis
  • 3.5 Regulatory Environment

4. Global Hardware Acceleration Market by Type

  • 4.1 Overview
  • 4.2 Attractiveness Analysis by Type
  • 4.3 Graphics Processing Unit: Trends and Forecast (2019-2031)
  • 4.4 Video Processing Unit: Trends and Forecast (2019-2031)
  • 4.5 AI Accelerator: Trends and Forecast (2019-2031)
  • 4.6 Regular Expression Accelerator: Trends and Forecast (2019-2031)
  • 4.7 Cryptographic Accelerator: Trends and Forecast (2019-2031)
  • 4.8 Others: Trends and Forecast (2019-2031)

5. Global Hardware Acceleration Market by Application

  • 5.1 Overview
  • 5.2 Attractiveness Analysis by Application
  • 5.3 Deep Learning Training: Trends and Forecast (2019-2031)
  • 5.4 Public Cloud Inference: Trends and Forecast (2019-2031)
  • 5.5 Enterprise Cloud Inference: Trends and Forecast (2019-2031)
  • 5.6 Others: Trends and Forecast (2019-2031)

6. Regional Analysis

  • 6.1 Overview
  • 6.2 Global Hardware Acceleration Market by Region

7. North American Hardware Acceleration Market

  • 7.1 Overview
  • 7.2 North American Hardware Acceleration Market by Type
  • 7.3 North American Hardware Acceleration Market by Application
  • 7.4 United States Hardware Acceleration Market
  • 7.5 Mexican Hardware Acceleration Market
  • 7.6 Canadian Hardware Acceleration Market

8. European Hardware Acceleration Market

  • 8.1 Overview
  • 8.2 European Hardware Acceleration Market by Type
  • 8.3 European Hardware Acceleration Market by Application
  • 8.4 German Hardware Acceleration Market
  • 8.5 French Hardware Acceleration Market
  • 8.6 Spanish Hardware Acceleration Market
  • 8.7 Italian Hardware Acceleration Market
  • 8.8 United Kingdom Hardware Acceleration Market

9. APAC Hardware Acceleration Market

  • 9.1 Overview
  • 9.2 APAC Hardware Acceleration Market by Type
  • 9.3 APAC Hardware Acceleration Market by Application
  • 9.4 Japanese Hardware Acceleration Market
  • 9.5 Indian Hardware Acceleration Market
  • 9.6 Chinese Hardware Acceleration Market
  • 9.7 South Korean Hardware Acceleration Market
  • 9.8 Indonesian Hardware Acceleration Market

10. ROW Hardware Acceleration Market

  • 10.1 Overview
  • 10.2 ROW Hardware Acceleration Market by Type
  • 10.3 ROW Hardware Acceleration Market by Application
  • 10.4 Middle Eastern Hardware Acceleration Market
  • 10.5 South American Hardware Acceleration Market
  • 10.6 African Hardware Acceleration Market

11. Competitor Analysis

  • 11.1 Product Portfolio Analysis
  • 11.2 Operational Integration
  • 11.3 Porter's Five Forces Analysis
    • Competitive Rivalry
    • Bargaining Power of Buyers
    • Bargaining Power of Suppliers
    • Threat of Substitutes
    • Threat of New Entrants
  • 11.4 Market Share Analysis

12. Opportunities & Strategic Analysis

  • 12.1 Value Chain Analysis
  • 12.2 Growth Opportunity Analysis
    • 12.2.1 Growth Opportunities by Type
    • 12.2.2 Growth Opportunities by Application
  • 12.3 Emerging Trends in the Global Hardware Acceleration Market
  • 12.4 Strategic Analysis
    • 12.4.1 New Product Development
    • 12.4.2 Certification and Licensing
    • 12.4.3 Mergers, Acquisitions, Agreements, Collaborations, and Joint Ventures

13. Company Profiles of the Leading Players Across the Value Chain

  • 13.1 Competitive Analysis
  • 13.2 Advanced Micro Devices
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.3 Intel Corporation
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.4 Lenovo Group
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.5 Nvidia Corporation
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.6 IBM Corporation
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.7 Xilinx
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing
  • 13.8 Oracle Corporation
    • Company Overview
    • Hardware Acceleration Business Overview
    • New Product Development
    • Merger, Acquisition, and Collaboration
    • Certification and Licensing

14. Appendix

  • 14.1 List of Figures
  • 14.2 List of Tables
  • 14.3 Research Methodology
  • 14.4 Disclaimer
  • 14.5 Copyright
  • 14.6 Abbreviations and Technical Units
  • 14.7 About Us
  • 14.8 Contact Us
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