Novel Stochastic Computing for Energy-Efficient Image Processors

Abstract

Stochastic computing, which is based on probability, involves a trade-off between accuracy and power and is a promising solution for energy-efficiency in error-tolerance designs. In this paper, adder and multiplier circuits based on the proposed stochastic computing architecture are studied and analyzed. First, we propose an efficient yet simple stochastic computation technique for multipliers and adders by exchanging the wires used for their operation. The results demonstrate that the proposed design reduces the relative error in computation compared with the conventional designs and has smaller area compared to conventional designs. Then, a new energy-efficient and high-performance stochastic adder with acceptable error metrics is investigated. The proposed multiplier shows better error metrics than other existing stochastic multipliers, and significantly improves area utilization and power consumption compared to the exact binary multiplier. Finally, we apply the proposed stochastic architecture to an edge detection algorithm and achieve a significant reduction in area utilization (64%) and power consumption (96%). It is therefore demonstrated that the proposed stochastic architecture is suitable for energy-efficient hardware designs.

Emerging Artificial Neuron Devices for Probabilistic Computing

“…This is because the response variability of cortical neurons observed in electrophysiological recordings has been well-explained in terms of probabilistic computation (Shadlen and Newsome, 1998). To date, stochastic computing algorithms based on restricted Boltzmann machine (Jordan et al, 2019) and Bayesian inference (Sountsov and Miller, 2015) have exhibited remarkable advantages in edge detection (Joe and Kim, 2019), traffic prediction (Sun X. et al, 2020), and the complex prediction of protein functions (Zou et al, 2017). However, the existing stochastic neural networks remain at quasistochastic states and are accelerated by the central processing unit or graphic processing unit.…”

Using Stochastic Computing for Virtual Screening Acceleration

“…In this particular context, stochastic computing (SC) arises as a potential alternative [30,31]. SC has been used to develop many different complex tasks, such as the implementation of Bayesian classifiers [32,33], image processing [34,35], or the implementation of neuromorphic circuits [36,37]. Data representation within the SC paradigm is performed in a probabilistic way with the use of Boolean quantities, which switch randomly over time.…”

Fast and exact multiple‐input unary‐to‐binary multiplier with variable precision for stochastic computing

“…Probabilistic interpretation of stochastic bit streams also leads to a non-deterministic result which in most cases is just an estimated value. Thus stochastic computing is only applied in fields that do not require high accuracy like image processing [2,3].…”