3,797 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
Dynamic Security-aware Routing for Zone-based data Protection in Multi-Processor System-on-Chips
In this work, we propose a NoC which enforces the
encapsulation of sensitive traffic inside the asymmetrical security
zones while using minimal and non-minimal paths. The NoC
routes guarantee that the sensitive traffic is communicated only
through the trusted nodes which belong to the security zone.
As the shape of the zones may change during operation, the
sensitive traffic must be routed through low-risk paths. We test
our proposal and we show that our solution can be an efficient
and scalable alternative for enforce the data protection inside the
MPSoC
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
Modeling high-performance wormhole NoCs for critical real-time embedded systems
Manycore chips are a promising computing platform to cope with the increasing performance needs of critical real-time embedded systems (CRTES). However, manycores adoption by CRTES industry requires understanding task's timing behavior when their requests use manycore's network-on-chip (NoC) to access hardware shared resources. This paper analyzes the contention in wormhole-based NoC (wNoC) designs - widely implemented in the high-performance domain - for which we introduce a new metric: worst-contention delay (WCD) that captures wNoC impact on worst-case execution time (WCET) in a tighter manner than the existing metric, worst-case traversal
time (WCTT). Moreover, we provide an analytical model of the WCD that requests can suffer in a wNoC and we validate it against wNoC designs resembling those in the Tilera-Gx36 and the Intel-SCC 48-core processors. Building on top of our WCD analytical model, we analyze the impact on WCD that different design parameters such as the number of virtual channels, and we make a set of recommendations on what wNoC setups to use in the context of CRTES.Peer ReviewedPostprint (author's final draft
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