A Dynamic Stochastic Computing-Based Approach to Edge Inference for Convolutional Neural Networks

Abstract

Deep neural networks achieve state-of-the-art performance in many vision tasks but require substantial computational resources and energy, making their deployment on edge devices and embedded platforms challenging. Several approaches have been proposed to address this challenge, including fixed-point arithmetic, stochastic computing (SC), and dynamic stochastic computing (DSC). This work presents a comparative analysis of these approaches using a convolutional neural network (CNN) for MNIST digit classification. The study compares floating-point inference, fixed-point quantized inference, stochastic computing using bitstream representations, and dynamic stochastic computing using accumulation with stochastic integrators. Performance is evaluated in terms of classification accuracy, cross-entropy loss, runtime latency, and throughput across different bit-widths and stochastic bitstream lengths. The results demonstrate the trade-offs among numerical precision, computational latency, and inference accuracy. Fixed-point implementations provide high throughput with minimal accuracy degradation at moderate bit-widths, whereas stochastic computing reduces arithmetic complexity but requires longer bitstreams to achieve comparable accuracy. Dynamic stochastic computing exhibits improved convergence characteristics compared with conventional stochastic computing. These results indicate that dynamic stochastic computing can serve as an efficient alternative for the design and implementation of neural-network accelerators.