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뉴로모픽 프로세서(2021-2022년) : Kisaco 리더십 차트

Kisaco Leadership Chart on Neuromorphic Processors 2021-22

리서치사 Kisaco Research Limited
발행일 2021년 05월 상품 코드 1007523
페이지 정보 영문 39 Pages
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US $ 4,999 ₩ 5,933,000 PDF (Single User License)


뉴로모픽 프로세서(2021-2022년) : Kisaco 리더십 차트 Kisaco Leadership Chart on Neuromorphic Processors 2021-22
발행일 : 2021년 05월 페이지 정보 : 영문 39 Pages

본 상품은 영문 자료로 한글과 영문목차에 불일치하는 내용이 있을 경우 영문을 우선합니다. 정확한 검토를 위해 영문목차를 참고해주시기 바랍니다.

뉴로모픽 컴퓨팅은 인간의 뇌와 직접적인 생물학적 링크를 가진 기술에 근거한 인공지능(AI) 연구로부터 태어났습니다. 뇌는 전기적인 스파이크를 사용하여 뉴런간 신호를 송신하는 아날로그 시스템입니다. 프로세서에 뉴로모픽 라벨을 선택하는 많은 벤더는 아날로그 시스템(일반적으로 전기 회로)으로 스파이킹 뉴럴 네트워크(SNN)를 사용하고 있습니다.

뉴로모픽(Neuromorphic)을 기존 AI와 구별하는 구체적인 포인트에 대해 설명했으며, 뉴로모픽 벤더 평가, 벤더 프로파일 등의 정보를 전해드립니다.

목차

  • Kisaco Research 견해
  • 동기
  • 주요 조사 결과
  • 솔루션 분석 : 뉴로모픽 프로세서
  • 기술 상황
  • 시장 구도
  • 솔루션 분석 : 벤더 비교
  • 뉴로모픽 프로세서에 대한 Kisaco 리더십 차트(KLC)
  • 뉴로모픽 프로세서 벤더 비교
  • 뉴로모픽 프로세서에 대한 KLC
  • 벤더 분석
  • AIStorm, Kisaco 평가 : 리더
  • Kisaco 평가
  • Aspinity, Kisaco 평가 : KLC 불참을 선택
  • BrainChip, Kisaco 평가 : 리더
  • Kisaco 평가
  • iniVation, Kisaco 평가 : KLC 불참을 선택
  • Innatera Nanosystems, Kisaco 평가 : 신규 기업
  • Kisaco 평가
  • Intel, Kisaco 평가 : KLC 불참을 선택
  • Rain Neuromorphics, Kisaco 평가 : 이노베이터
  • 서론
  • The Rain Analog Processing Unit (APU)
  • Taping out APU chips
  • The Rain energy equilibrium algorithm for neural learning
  • The Rain APU 3D synaptic architecture
  • Kisaco 평가
  • SynSense, Kisaco 평가 : 경쟁자
  • Kisaco 평가
  • 부록
  • 벤더 솔루션 선정
  • 선정 기준
  • 조사 방법
  • KLC의 정의
  • Kisaco Research 평가
  • 참고 자료
  • 사의
  • 저자
  • Kisaco Research의 분석 네트워크
  • 저작권 표시 및 면책사항
LSH 21.07.07

Motivation

Neuromorphic computing arises out of artificial intelligence (AI) research based on technology that has direct biological links with the human brain. The brain is an analog system that uses electrical spikes to transmit signals between neurons, similarly many vendors that choose the neuromorphic label for their processors use spiking neural networks (SNNs) in an analog system, typically electric circuits. However, other such vendors choose to use digital devices with a SNN, and yet again others use an analog device with non-spiking, continuous value signal neural networks.

Neuromorphic computing emerged in the 1990s but has had a slow evolution due to the challenges in training neural networks without use of a global learning rule, such as backpropagation. Backpropagation is critical in (non-spiking) deep learning neural networks, and it uses information at the output of the network to update neurons (more exactly the synapse weights) upstream in the network. To our best understanding at time of writing the human brain does not use a global learning rule and it has taken time for local learning rules to emerge for neuromorphic architectures, with success in the last two years, and this has given birth to a surge in startups in this space.

To find the common ground that can be pinned to the neuromorphic label there are two key characteristics: low power consumption and high efficiency, typically in the form of highly sparse connectivity - both characteristics of the human brain. We delve deeper into what exactly distinguishes neuromorphic from the traditional AI in this report. We also assess neuromorphic vendors with processors that span the range of possible architectures and learning rules. The Kisaco Leadership Chart (KLC) compares five of the pioneering vendors side by side: AIStorm, BrainChip, Innatera Nanosystems, Rain Neuromorphics, and SynSense. In addition to our in-depth profiles on these vendors, we have three more vendors profiled in-depth: Aspinity, Intel, and Inivation.

What you will learn:

  • How neuromorphic processors differ from other AI processors on the market.
  • Which is the strongest market segment for neuromorphic processors.
  • Our report has assessed five neuromorphic processor vendors and we provide a high-level heatmap on the key features available
  • We compare the processors from the five participating vendors side by side and assess these in our Kisaco Leadership Chart.
  • We provide an in-depth profile on each of the participating vendors together with three strengths and three weaknesses.

Table of Contents

  • Kisaco Research View
  • Motivation
  • Key findings
  • Solution Analysis: Neuromorphic processors
  • Technology landscape
  • Market landscape
  • Solution analysis: vendor comparisons
  • Kisaco Leadership Chart on Neuromorphic Processors 2020-21
  • Neuromorphic processor vendor comparisons
  • The KLC chart for neuromorphic processors
  • Vendor analysis
  • AIStorm, Kisaco evaluation: Leader
  • Kisaco Assessment
  • Aspinity, Kisaco evaluation: chose not to participate in KLC
  • BrainChip, Kisaco evaluation: Leader
  • Kisaco Assessment
  • iniVation, Kisaco evaluation: chose not to participate in KLC
  • Innatera Nanosystems, Kisaco evaluation: Emerging Player
  • Kisaco Assessment
  • Intel, Kisaco evaluation: chose not to participate in KLC
  • Rain Neuromorphics, Kisaco evaluation: Innovator
  • Introduction
  • The Rain Analog Processing Unit (APU)
  • Taping out APU chips
  • The Rain energy equilibrium algorithm for neural learning
  • The Rain APU 3D synaptic architecture
  • Kisaco Assessment
  • SynSense, Kisaco evaluation: Contender
  • Kisaco Assessment
  • Appendix
  • Vendor solution selection
  • Inclusion criteria
  • Methodology
  • Definition of the KLC
  • Kisaco Research ratings
  • Further reading
  • Acknowledgements
  • Author
  • Kisaco Research Analysis Network
  • Copyright notice and disclaimer

Figures

  • Figure 1: Comparing the brain, neuromorphic chip, and GPU in AI inference mode.
  • Figure 2: Comparing the KLC vendors on key technology features.
  • Figure 3: Heat map analysis of participating vendor technical features.
  • Figure 4: Kisaco Leadership Chart on Neuromorphic Processors 2020-21.
  • Figure 5: Kisaco Leadership Chart on Neuromorphic Processors 2020-21: ranking of vendors.
  • Figure 6: Comparing digitization of input with AIStorm's AI-in-Sensor.
  • Figure 7: AIStorm imager with "always on" cascaded wake-on approach.
  • Figure 8: Aspinity AnalogML typical use case.
  • Figure 9: Aspinity AnalogML core.
  • Figure 10: BrainChip Akida NPU architecture and IP solution.
  • Figure 11: BrainChip Akida software development environment and training workflow.
  • Figure 12: Inivation sensors only capture image changes.
  • Figure 13: Innatera spiking neural processor architecture.
  • Figure 14: Innatera spiking neural processor: segment zoom view.
  • Figure 15: Audio processing with a temporal feedforward SNN on the Innatera SNP.
  • Figure 16: Loihi benchmarks: Recurrent networks with bio-inspired properties give the best results.
  • Figure 17: Loihi Research Systems currently available.
  • Figure 18: Loihi projects pursued by INRC members.
  • Figure 19: Efficient sensing and pattern learning.
  • Figure 20: Rain's Analog Processing Units (APUs).
  • Figure 21: Rain APU 3D architecture vs traditional 2D crossbar.
  • Figure 22: SynSense hardware families.
  • Figure 23: CNN based processing stack. Backpropagation-based training of visual features.
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