第28届IEEE异步电路与系统国际会议（ASYNC）即将于7月16日至19日在中国北京举行。这是会议首次落地中国，由清华大学主办，SynSense时识科技及北京信息科学与技术国家研究中心支持，旨在为来自学术界和工业界的研究人员和工程师提供高质量的国际论坛，展示全球异步VLSI设计的最新见解和成果。

本次大会中，SynSense时识科技联合创始人、苏黎世大学/苏黎世联邦理工Giacomo Indiveri教授将担任开幕式主讲嘉宾，SynSense时识科技创始人兼CEO乔宁博士担任工业联络主席，SynSense时识科技技术团队将讲解纯异步电路设计的全球首款感算一体动态视觉SoC Speck™并展示最新动态视觉demo。

以事件驱动、低功耗及高能效为特点，异步设计在大规模分布式众核系统、低功耗和高能效电路系统、类脑电路和系统等方面发挥着重要作用。生物系统除了天然地以事件驱动和异步方式进行计算之外，其特性和时间动态密切相关，模拟真实神经元和突触的混合模拟/数字硬件架构代表了一种有前景的技术，成为了学术及工业界共同的展望方向。

亮点前瞻1

主旨报告

Mike Davies

Neuromorphic Computing

Laboratory, Intel Corporation

Title: Go Big or Go Home

Abstract: After decades of research and a handful of commercial applications, asynchronous design remains a niche methodology at the fringe of the semiconductor industry. The reasons for this are well known: lack of EDA tool support, lack of standardization, limited literature quantifying sync-versus-async tradeoffs, and a “black art” reputation with little mainstream awareness. Asynchronous circuits can outperform synchronous circuits, but in most cases the gains are insufficient to overcome the costs of deviating from standard synchronous methods. For this to change and for asynchronous design to thrive, a compelling killer app is needed – a new class of computing device in which the benefits of asynchrony overwhelm the cost of synchronization at the circuit level. Biological neural circuits in the brains of animals have that characteristic, perhaps the only known example. In this talk I will describe the deep synergies between asynchronous design and neuromorphic chips inspired by biological neural networks. Arguably the fates of these two technologies are fundamentally.

Giacomo INDIVERI

University of Zurich and ETH Zurich, Switzerland

Title: Brain-inspired routing in mixed-signal neuromorphic processor

For many edge-computing tasks that require real-time processing of sensory data and closed-loop interactions with the environment, conventional ANN accelerators cannot match the performance and efficiency of animal brains. One of the reasons for this gap is that neural computation in biological systems is organized in a way that is very different from the way it is implemented in today’s deep network accelerators. In addition to being naturally event driven and asynchronous, neural computation in biological systems is tightly linked to the physics of their computing elements and to their temporal dynamics. Mixed-signal brain-inspired hardware architectures that emulate the biophysics of real neurons and synapses represent a promising technology for implementing alternative computing paradigms that bridge this gap.In this talk I will present hybrid analog/digital electronic circuits that directly emulate the biophysics of neural systems and present brain-inspired routing schemes multi-core architectures that support small-world network connectivity and minimize memory requirements.

Yvain Thonnart

CEA-List, France

Title: The ANOC Asynchronous Communication Architecture: a Retrospective on a 15-year Circuit Roadmap

Kanwen(Kevin) Wang

Huawei Technologies Co., Ltd., China

Title:Our Practice and Expectations on Asynchronous Design

 

亮点前瞻2 

Special Session

Huaqiang Wu

Tsinghua University,China

Title: Memristor-based Energy-Efficient Neuromorphic Computing

Elisabetta Chicca

University of Groningen, The Netherlands

Title: Biologically Realistic Learning in Full Custom CMOS Asynchronous Systems

Gang Pan

Zhejiang University

Title: Neuromorphic Computer: Progresses, Challenges, and Practices

Tutorial

Rajit Manohar 

Computer Systems Lab at Yale, USA

Title: An ASIC Flow for Asynchronous Logic

 

亮点前瞻3

Session: Asynchronous Pipelines, Architectures, CPUs, and Circuits

A Smart Event-based Vision Sensor with a 320K Neuron Convolutional Neuronal Network Processing Pipeline

The nature of event-driven sensing and computing facilitates the development of lightweight, standalone AI applications with exceptionally low latency and a minimal power consumption. This paper presents a demonstration of an end-to-end, fully event-driven spiking neural network (SNN) inference using a smart vision System on Chip (SoC) called Speck™. Speck™, the world’s first neuromorphic chip, integrates a dynamic vision sensor and an SNN ASIC on a single die. In this paper, we demonstrate that Speck™ is highly optimized for loaded SNN and event-based vision signals, enabling effective handling of various real-life vision tasks by leveraging its asynchronous structure. Moreover, we showcase our comprehensive neuromorphic software pipeline, which significantly simplifies the development of neuromorphic applications based on Speck™.

信息及图片来源：ASYNC、亚昂学术