DeepAI
Log In Sign Up

AMR-MUL: An Approximate Maximally Redundant Signed Digit Multiplier

08/29/2022
by   Saba Amanollahi, et al.
0

In this paper, we present an energy-efficient, yet high-speed approximate maximally redundant signed digit (MRSD) multiplier (called AMR-MUL) based on a parallel structure. For the reduction stage, we suggest several approximate Full-Adder (FA) reduction cells with average positive and negative errors obtained by simplifying the structure of an exact FA cell. The optimum selection of these cells for each partial product reduction stage provides the lowest possible error, turning this task into a design space exploration problem. We also provide a branch-and-bound design space exploration algorithm to find the optimal assignment of reduction cells based on a predefined constraint (i.e., the width of the approximate part) by the user. The effectiveness of the proposed (Radix-16) multiplier design is assessed under different digit counts and approximate border column. The results show that the energy consumption of the MRSD multiplier is reduced by 7x at the cost of a 1.6

READ FULL TEXT

page 1

page 2

page 3

07/25/2022

Energy-efficient DNN Inference on Approximate Accelerators Through Formal Property Exploration

Deep Neural Networks (DNNs) are being heavily utilized in modern applica...
07/20/2021

Positive/Negative Approximate Multipliers for DNN Accelerators

Recent Deep Neural Networks (DNNs) managed to deliver superhuman accurac...
07/04/2019

Low-power and Reliable Solid-state Drive with Inverted Limited Weight Coding

In this work, we propose a novel coding scheme which based on the charac...
02/12/2018

Quasi-Optimal Partial Order Reduction

A dynamic partial order reduction (DPOR) algorithm is optimal when it al...
10/24/2017

Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection

We consider the problem of approximate reduction of non-deterministic au...
10/24/2017

Approximate Reduction of Finite Automata for High-Speed Network Intrusion Detection (Technical Report)

We consider the problem of approximate reduction of non-deterministic au...