A scalable multi-core architecture with heterogeneous memory structures for Dynamic Neuromorphic Asynchronous Processors (DYNAPs)

Neuromorphic computing systems comprise networks of neurons that use asynchronous events for both computation and communication. This type of representation offers several advantages in terms of bandwidth and power consumption in neuromorphic electronic systems. However, managing the traffic of asynchronous events in large scale systems is a daunting task, both in terms of circuit complexity and memory requirements. Here we present a novel routing methodology that employs both hierarchical and mesh routing strategies and combines heterogeneous memory structures for minimizing both memory requirements and latency, while maximizing programming flexibility to support a wide range of event-based neural network architectures, through parameter configuration. We validated the proposed scheme in a prototype multi-core neuromorphic processor chip that employs hybrid analog/digital circuits for emulating synapse and neuron dynamics together with asynchronous digital circuits for managing the address-event traffic. We present a theoretical analysis of the proposed connectivity scheme, describe the methods and circuits used to implement such scheme, and characterize the prototype chip. Finally, we demonstrate the use of the neuromorphic processor with a convolutional neural network for the real-time classification of visual symbols being flashed to a dynamic vision sensor (DVS) at high speed.Comment: 17 pages, 14 figure

Low Power Processor Architectures and Contemporary Techniques for Power Optimization – A Review

The technological evolution has increased the number of transistors for a given die area significantly and increased the switching speed from few MHz to GHz range. Such inversely proportional decline in size and boost in performance consequently demands shrinking of supply voltage and effective power dissipation in chips with millions of transistors. This has triggered substantial amount of research in power reduction techniques into almost every aspect of the chip and particularly the processor cores contained in the chip. This paper presents an overview of techniques for achieving the power efficiency mainly at the processor core level but also visits related domains such as buses and memories. There are various processor parameters and features such as supply voltage, clock frequency, cache and pipelining which can be optimized to reduce the power consumption of the processor. This paper discusses various ways in which these parameters can be optimized. Also, emerging power efficient processor architectures are overviewed and research activities are discussed which should help reader identify how these factors in a processor contribute to power consumption. Some of these concepts have been already established whereas others are still active research areas. © 2009 ACADEMY PUBLISHER

低電力非同期回路の面積高効率化設計

Tohoku University亀山充隆課

CAD Tools for Synthesis of Sleep Convention Logic

This dissertation proposes an automated flow for the Sleep Convention Logic (SCL) asynchronous design style. The proposed flow synthesizes synchronous RTL into an SCL netlist. The flow utilizes commercial design tools, while supplementing missing functionality using custom tools. A method for determining the performance bottleneck in an SCL design is proposed. A constraint-driven method to increase the performance of linear SCL pipelines is proposed. Several enhancements to SCL are proposed, including techniques to reduce the number of registers and total sleep capacitance in an SCL design

Design and performance optimization of asynchronous networks-on-chip

As digital systems continue to grow in complexity, the design of conventional synchronous systems is facing unprecedented challenges. The number of transistors on individual chips is already in the multi-billion range, and a greatly increasing number of components are being integrated onto a single chip. As a consequence, modern digital designs are under strong time-to-market pressure, and there is a critical need for composable design approaches for large complex systems. In the past two decades, networks-on-chip (NoC’s) have been a highly active research area. In a NoC-based system, functional blocks are first designed individually and may run at different clock rates. These modules are then connected through a structured network for on-chip global communication. However, due to the rigidity of centrally-clocked NoC’s, there have been bottlenecks of system scalability, energy and performance, which cannot be easily solved with synchronous approaches. As a result, there has been significant recent interest in combing the notion of asynchrony with NoC designs. Since the NoC approach inherently separates the communication infrastructure, and its timing, from computational elements, it is a natural match for an asynchronous paradigm. Asynchronous NoC’s, therefore, enable a modular and extensible system composition for an ‘object-orient’ design style. The thesis aims to significantly advance the state-of-art and viability of asynchronous and globally-asynchronous locally-synchronous (GALS) networks-on-chip, to enable high-performance and low-energy systems. The proposed asynchronous NoC’s are nearly entirely based on standard cells, which eases their integration into industrial design flows. The contributions are instantiated in three different directions. First, practical acceleration techniques are proposed for optimizing the system latency, in order to break through the latency bottleneck in the memory interfaces of many on-chip parallel processors. Novel asynchronous network protocols are proposed, along with concrete NoC designs. A new concept, called ‘monitoring network’, is introduced. Monitoring networks are lightweight shadow networks used for fast-forwarding anticipated traffic information, ahead of the actual packet traffic. The routers are therefore allowed to initiate and perform arbitration and channel allocation in advance. The technique is successfully applied to two topologies which belong to two different categories – a variant mesh-of-trees (MoT) structure and a 2D-mesh topology. Considerable and stable latency improvements are observed across a wide range of traffic patterns, along with moderate throughput gains. Second, for the first time, a high-performance and low-power asynchronous NoC router is compared directly to a leading commercial synchronous counterpart in an advanced industrial technology. The asynchronous router design shows significant performance improvements, as well as area and power savings. The proposed asynchronous router integrates several advanced techniques, including a low-latency circular FIFO for buffer design, and a novel end-to-end credit-based virtual channel (VC) flow control. In addition, a semi-automated design flow is created, which uses portions of a standard synchronous tool flow. Finally, a high-performance multi-resource asynchronous arbiter design is developed. This small but important component can be directly used in existing asynchronous NoC’s for performance optimization. In addition, this standalone design promises use in opening up new NoC directions, as well as for general use in parallel systems. In the proposed arbiter design, the allocation of a resource to a client is divided into several steps. Multiple successive client-resource pairs can be selected rapidly in pipelined sequence, and the completion of the assignments can overlap in parallel. In sum, the thesis provides a set of advanced design solutions for performance optimization of asynchronous and GALS networks-on-chip. These solutions are at different levels, from network protocols, down to router- and component-level optimizations, which can be directly applied to existing basic asynchronous NoC designs to provide a leap in performance improvement

Exploration and Design of High Performance Variation Tolerant On-Chip Interconnects

Siirretty Doriast

Peephole optimization of asynchronous networks through process composition and burst-mode machine generation

Journal ArticleIn this paper, we discuss the problem of improving the efficiency of macromodule networks generated through asynchronous high level synthesis. We compose the behaviors of the modules in the sub-network being optimized using Dill's trace-theoretic operators to get a single behavioral description for the whole sub-network. From the composite trace structures so obtained, we obtain interface state graphs (ISG) (as described by Sutherland, Sproull, and Molnar), encode the ISGs to obtain encoded ISGs (EISGs), and then apply a procedure we have developed called Burst-mode machine reduction (BM-reduction) to obtain burstmode machines from EISGs. We then synthesize burst-mode machine circuits (currently) using the tool of Ken Yun (Stanford). We can report significant area- and time-improvements on a number of examples, as a result of our optimization method

Hardware Implementation of Deep Network Accelerators Towards Healthcare and Biomedical Applications

With the advent of dedicated Deep Learning (DL) accelerators and neuromorphic processors, new opportunities are emerging for applying deep and Spiking Neural Network (SNN) algorithms to healthcare and biomedical applications at the edge. This can facilitate the advancement of the medical Internet of Things (IoT) systems and Point of Care (PoC) devices. In this paper, we provide a tutorial describing how various technologies ranging from emerging memristive devices, to established Field Programmable Gate Arrays (FPGAs), and mature Complementary Metal Oxide Semiconductor (CMOS) technology can be used to develop efficient DL accelerators to solve a wide variety of diagnostic, pattern recognition, and signal processing problems in healthcare. Furthermore, we explore how spiking neuromorphic processors can complement their DL counterparts for processing biomedical signals. After providing the required background, we unify the sparsely distributed research on neural network and neuromorphic hardware implementations as applied to the healthcare domain. In addition, we benchmark various hardware platforms by performing a biomedical electromyography (EMG) signal processing task and drawing comparisons among them in terms of inference delay and energy. Finally, we provide our analysis of the field and share a perspective on the advantages, disadvantages, challenges, and opportunities that different accelerators and neuromorphic processors introduce to healthcare and biomedical domains. This paper can serve a large audience, ranging from nanoelectronics researchers, to biomedical and healthcare practitioners in grasping the fundamental interplay between hardware, algorithms, and clinical adoption of these tools, as we shed light on the future of deep networks and spiking neuromorphic processing systems as proponents for driving biomedical circuits and systems forward.Comment: Submitted to IEEE Transactions on Biomedical Circuits and Systems (21 pages, 10 figures, 5 tables