1,006 research outputs found
MorphIC: A 65-nm 738k-Synapse/mm Quad-Core Binary-Weight Digital Neuromorphic Processor with Stochastic Spike-Driven Online Learning
Recent trends in the field of neural network accelerators investigate weight
quantization as a means to increase the resource- and power-efficiency of
hardware devices. As full on-chip weight storage is necessary to avoid the high
energy cost of off-chip memory accesses, memory reduction requirements for
weight storage pushed toward the use of binary weights, which were demonstrated
to have a limited accuracy reduction on many applications when
quantization-aware training techniques are used. In parallel, spiking neural
network (SNN) architectures are explored to further reduce power when
processing sparse event-based data streams, while on-chip spike-based online
learning appears as a key feature for applications constrained in power and
resources during the training phase. However, designing power- and
area-efficient spiking neural networks still requires the development of
specific techniques in order to leverage on-chip online learning on binary
weights without compromising the synapse density. In this work, we demonstrate
MorphIC, a quad-core binary-weight digital neuromorphic processor embedding a
stochastic version of the spike-driven synaptic plasticity (S-SDSP) learning
rule and a hierarchical routing fabric for large-scale chip interconnection.
The MorphIC SNN processor embeds a total of 2k leaky integrate-and-fire (LIF)
neurons and more than two million plastic synapses for an active silicon area
of 2.86mm in 65nm CMOS, achieving a high density of 738k synapses/mm.
MorphIC demonstrates an order-of-magnitude improvement in the area-accuracy
tradeoff on the MNIST classification task compared to previously-proposed SNNs,
while having no penalty in the energy-accuracy tradeoff.Comment: This document is the paper as accepted for publication in the IEEE
Transactions on Biomedical Circuits and Systems journal (2019), the
fully-edited paper is available at
https://ieeexplore.ieee.org/document/876400
Early analysis of VLSI systems with packaging considerations
There is an explosive growth in the size of the VLSI (Very Large Scale Integration) systems today. Microelectronic system designers are packing millions of transistors in a single IC chip. Packaging techniques like Multi-chip module (MCM) and flip-chip bonding offer faster interconnects and IC\u27s capable of accommodating a larger number of inputs and outputs. The complexity of today\u27s designs and the availability of advanced packaging techniques call for an early analysis of the system based on estimation of system parameters to select from a wide choice of circuit partitioning, architecture alternatives and packaging options which give the best cost/performance.
A procedure for the early analysis of VLSI systems under packaging considerations has been developed and implemented in this dissertation work. The early analysis tool was used to evaluate the inter-relationship between partitioning and packaging and to determine the best system design considering cost, size and delays. The functional unit level description of a 750,000-transistor MicroSparc processor was studied using an exhaustive search technique. The early analysis performed on the MicroSparc design suggested that the three chip multi-chip design using flip-chip IC\u27s interconnected on a MCM-D substrate is the most cost effective. An early bond pitch analysis performed using the tool concluded that a 250-micron bond pitch is the best choice for the multi-chip MicroSparc designs. The tool was also used to perform an early cache analysis which showed that the use of separate memory and logic processes made it feasible to design the MicroSparc design with larger cache sizes than the use of a combined logic and memory process. The designs based on the separate processes gave equivalent or better performance than the design candidates with smaller cache sizes. Future extensions of the procedure are also outlined here
Printed Circuit Board (PCB) design process and fabrication
This module describes main characteristics of Printed Circuit Boards (PCBs). A brief history of PCBs is introduced in the first chapter. Then, the design processes and the fabrication of PCBs are addressed and finally a study case is presented in the last chapter of the module.Peer ReviewedPostprint (published version
A procedural method for the efficient implementation of full-custom VLSI designs
An imbedded language system for the layout of very large scale integration (VLSI) circuits is examined. It is shown that through the judicious use of this system, a large variety of circuits can be designed with circuit density and performance comparable to traditional full-custom design methods, but with design costs more comparable to semi-custom design methods. The high performance of this methodology is attributable to the flexibility of procedural descriptions of VLSI layouts and to a number of automatic and semi-automatic tools within the system
A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems
In this paper we present a methodological framework that meets novel
requirements emerging from upcoming types of accelerated and highly
configurable neuromorphic hardware systems. We describe in detail a device with
45 million programmable and dynamic synapses that is currently under
development, and we sketch the conceptual challenges that arise from taking
this platform into operation. More specifically, we aim at the establishment of
this neuromorphic system as a flexible and neuroscientifically valuable
modeling tool that can be used by non-hardware-experts. We consider various
functional aspects to be crucial for this purpose, and we introduce a
consistent workflow with detailed descriptions of all involved modules that
implement the suggested steps: The integration of the hardware interface into
the simulator-independent model description language PyNN; a fully automated
translation between the PyNN domain and appropriate hardware configurations; an
executable specification of the future neuromorphic system that can be
seamlessly integrated into this biology-to-hardware mapping process as a test
bench for all software layers and possible hardware design modifications; an
evaluation scheme that deploys models from a dedicated benchmark library,
compares the results generated by virtual or prototype hardware devices with
reference software simulations and analyzes the differences. The integration of
these components into one hardware-software workflow provides an ecosystem for
ongoing preparative studies that support the hardware design process and
represents the basis for the maturity of the model-to-hardware mapping
software. The functionality and flexibility of the latter is proven with a
variety of experimental results
A survey on scheduling and mapping techniques in 3D Network-on-chip
Network-on-Chips (NoCs) have been widely employed in the design of
multiprocessor system-on-chips (MPSoCs) as a scalable communication solution.
NoCs enable communications between on-chip Intellectual Property (IP) cores and
allow those cores to achieve higher performance by outsourcing their
communication tasks. Mapping and Scheduling methodologies are key elements in
assigning application tasks, allocating the tasks to the IPs, and organising
communication among them to achieve some specified objectives. The goal of this
paper is to present a detailed state-of-the-art of research in the field of
mapping and scheduling of applications on 3D NoC, classifying the works based
on several dimensions and giving some potential research directions
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