An Energy-Efficient RFET-Based Stochastic Computing Neural Network Accelerator

TL;DR

This paper introduces a reconfigurable RFET-based stochastic computing neural network accelerator that significantly reduces area, latency, and energy consumption compared to traditional FinFET-based designs.

Contribution

It presents a novel RFET-based architecture enabling highly efficient, compact stochastic computing components and a dedicated accelerator for neural networks.

Findings

Reduces hardware area by X%

Lowers energy consumption by Y%

Achieves Z times faster processing

Abstract

Stochastic computing (SC) offers significant reductions in hardware complexity for traditional convolutional neural networks(CNNs). However, despite its advantages, stochastic computing neural networks (SCNNs) often suffer from high resource consumption due to components such as stochastic number generators (SNGs) and accumulative parallel counters (APCs), which limit overall performance. This paper proposes a novel SCNN architecture leveraging reconfigurable field-effect transistors (RFETs). The inherent reconfigurability at the device level enables the design of highly efficient and compact SNGs, APCs, and other related essential components. Furthermore, a dedicated SCNN accelerator architecture is developed to facilitate system-level simulation. Based on accessible open-source standard cell libraries, experimental results demonstrate that the proposed RFET-based SCNN accelerator achieves significant reductions in area, latency, and energy consumption compared to its FinFET-based counterpart at the same technology node.