VLSI Implementation of Deep Neural Network Using Integral Stochastic Computing

The hardware implementation of deep neural networks (DNNs) has recently received tremendous attention: many applications in fact require high-speed operations that suit a hardware implementation. However, numerous elements and complex interconnections are usually required, leading to a large area occupation and copious power consumption. Stochastic computing has shown promising results for low-power area-efficient hardware implementations, even though existing stochastic algorithms require long streams that cause long latencies. In this paper, we propose an integer form of stochastic computation and introduce some elementary circuits. We then propose an efficient implementation of a DNN based on integral stochastic computing. The proposed architecture has been implemented on a Virtex7 FPGA, resulting in 45% and 62% average reductions in area and latency compared to the best reported architecture in literature. We also synthesize the circuits in a 65 nm CMOS technology and we show that the proposed integral stochastic architecture results in up to 21% reduction in energy consumption compared to the binary radix implementation at the same misclassification rate. Due to fault-tolerant nature of stochastic architectures, we also consider a quasi-synchronous implementation which yields 33% reduction in energy consumption w.r.t. the binary radix implementation without any compromise on performance.Comment: 11 pages, 12 figure

A highly parameterizable framework for Conditional Restricted Boltzmann Machine based workloads accelerated with FPGAs and OpenCL

© 2020 Elsevier. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/Conditional Restricted Boltzmann Machine (CRBM) is a promising candidate for a multidimensional system modeling that can learn a probability distribution over a set of data. It is a specific type of an artificial neural network with one input (visible) and one output (hidden) layer. Recently published works demonstrate that CRBM is a suitable mechanism for modeling multidimensional time series such as human motion, workload characterization, city traffic analysis. The process of learning and inference of these systems relies on linear algebra functions like matrix–matrix multiplication, and for higher data sets, they are very compute-intensive. In this paper, we present a configurable framework for CRBM based workloads for arbitrary large models. We show how to accelerate the learning process of CRBM with FPGAs and OpenCL, and we conduct an extensive scalability study for different model sizes and system configurations. We show significant improvement in performance/Watt for large models and batch sizes (from 1.51x up to 5.71x depending on the host configuration) when we use FPGA and OpenCL for the acceleration, and limited benefits for small models comparing to the state-of-the-art CPU solution.This work was supported by the European Research Council(ERC) under the European Union’s Horizon 2020 research andinnovation programme (grant agreements No 639595); the Min-istry of Economy of Spain under contract TIN2015-65316-P andGeneralitat de Catalunya, Spain under contract 2014SGR1051;the ICREA, Spain Academia program; the BSC-CNS Severo Ochoaprogram, Spain (SEV-2015-0493) and Intel Corporation, UnitedStatesPeer ReviewedPostprint (published version

On the Challenges of Physical Implementations of RBMs

Restricted Boltzmann machines (RBMs) are powerful machine learning models, but learning and some kinds of inference in the model require sampling-based approximations, which, in classical digital computers, are implemented using expensive MCMC. Physical computation offers the opportunity to reduce the cost of sampling by building physical systems whose natural dynamics correspond to drawing samples from the desired RBM distribution. Such a system avoids the burn-in and mixing cost of a Markov chain. However, hardware implementations of this variety usually entail limitations such as low-precision and limited range of the parameters and restrictions on the size and topology of the RBM. We conduct software simulations to determine how harmful each of these restrictions is. Our simulations are designed to reproduce aspects of the D-Wave quantum computer, but the issues we investigate arise in most forms of physical computation