表紙:ニューロモルフィックプロセッサー (2021年~2022年) :Kisacoリーダーシップチャート
市場調査レポート
商品コード
1007523

ニューロモルフィックプロセッサー (2021年~2022年) :Kisacoリーダーシップチャート

Kisaco Leadership Chart on Neuromorphic Processors 2021-22

出版日: | 発行: Kisaco Research Limited | ページ情報: 英文 39 Pages | 納期: 即日から翌営業日

価格
価格表記: USDを日本円(税抜)に換算
本日の銀行送金レート: 1USD=114.87円
ニューロモルフィックプロセッサー (2021年~2022年) :Kisacoリーダーシップチャート
出版日: 2021年05月13日
発行: Kisaco Research Limited
ページ情報: 英文 39 Pages
納期: 即日から翌営業日
  • 全表示
  • 概要
  • 目次
概要

ニューロモルフィックコンピューティングは、人間の脳と直接的な生物学的リンクを持つ技術に基づく人工知能(AI)の研究から生まれました。脳は、電気的なスパイクを使用してニューロン間で信号を送信するアナログシステムです。同様に、プロセッサーにニューロモルフィックラベルを選択する多くのベンダーは、アナログシステム(通常は電気回路)でスパイキングニューラルネットワーク(SNN)を使用しています。

当レポートでは、ニューロモルフィックを従来のAIと区別する具体的なポイントについて説明し、ニューロモルフィックベンダーについて評価し、各ベンダーのプロファイルを紹介します。

目次

  • Kisaco Researchの見解
  • 動機
  • 主な調査結果
  • ソリューション分析:ニューロモルフィックプロセッサー
  • 技術情勢
  • 市場情勢
  • ソリューション分析:ベンダーの比較
  • ニューロモルフィックプロセッサーに対するKisacoリーダーシップチャート (KLC)
  • ニューロモルフィックプロセッサーベンダーの比較
  • ニューロモルフィックプロセッサーに対するKLC
  • ベンダー分析
  • AIStorm、Kisacoの評価:リーダー
  • Kisacoのアセスメント
  • Aspinity、Kisacoの評価:KLCに参加しないことを選択
  • BrainChip、Kisacoの評価:リーダー
  • Kisacoのアセスメント
  • iniVation、Kisacoの評価:KLCに参加しないことを選択
  • Innatera Nanosystems、Kisacoの評価:新興企業
  • Kisacoのアセスメント
  • Intel、Kisacoの評価:KLCに参加しないことを選択
  • Rain Neuromorphics、Kisacoの評価:イノベーター
  • イントロダクション
  • Rainのアナログプロセッシングユニット(APU)
  • APUチップのテープアウト
  • Rainの神経学習エネルギー平衡アルゴリズム
  • RainのAPU3Dシナプスアーキテクチャー
  • Kisacoのアセスメント
  • SynSense、Kisacoの評価:コンテンダー
  • Kisacoのアセスメント
  • 付録
  • ベンダーソリューションの選定
  • 選定基準
  • 調査手法
  • KLCの定義
  • Kisaco Researchの評価
  • 参考資料
  • 謝意
  • 著者
  • Kisaco Researchによる分析ネットワーク
  • 著作権表示・免責事項
目次

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.