This article presents an overview of circuit-level techniques used for approximate computing
(AC), including both computation and data storage units. After providing some background …

Deep neural networks (DNNs) are a type of artificial intelligence models that are inspired by
the structure and function of the human brain, designed to process and learn from large …

This article investigates an energy-efficient accuracy-configurable Dadda (X-Dadda)
multiplier. The structure employs the voltage overscaling and approximate width setting as …

This paper investigates a runtime accuracy reconfigurable implementation of an energy
efficient deep learning accelerator. It is based on voltage overscaling (VOS) technique which …

Voltage overscaling has received extensive attention in the last decade as an attractive
paradigm for systems in which resulting timing errors and thus a loss in accuracy can be …

Many applications show tolerance to inaccuracies. These can be exploited to build faster
circuits with smaller area and lower power. This is particularly true for the hardware …

Triple modular redundancy (TMR) is one of the most common techniques in fault-tolerant
systems, in which the output is determined by a majority voter. However, the design diversity …

In this paper, a mixed-signal coarse-grained reconfigurable architecture (CGRA) for
accelerating inference in deep neural networks (DNNs) is presented. It is based on …

This article presents an error estimation technique for a data-flow graph (DFG)
representation of an approximate computing (AC) circuit. The technique, which may be used …

With the rapid reduction of CMOS process size, the FPGAs with high-silicon accumulation
technology are becoming more sensitive to aging effects. This reduces the reliability and …