A Novel Area Efficient Approximate Stochastic Computing Approach for Edge Devices

Recent advancements in approximate stochastic computing (ASC) hold great promise for achieving fast and energy-efficient computation in low-power edge devices utilizing system-on-chip (SoC) technology. ASC is particularly relevant in machine learning, AI and image processing applications, where the overall precision of computation can be intentionally reduced for fast and low-power approximate computation. This reduction in precision allows us to meet design constraints and performance requirements while still obtaining acceptable results. In this paper, we propose a novel area-efficient approach for stochastic approximate computing circuits and compare it with the existing approach in terms of truncation error.