Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing formal guarantees on the approximation error into an evolutionary circuit optimisation algorithm. The key idea is to employ a novel adaptive search strategy that drives the evolution towards promptly verifiable approximate circuits. As demonstrated in an extensive experimental evaluation including several structurally different arithmetic circuits and target precisions, the search strategy provides superior scalability and versatility with respect to various approximation scenarios. Our approach significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably-correct circuit approximations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
SONATA: Self-adaptive Evolutionary Framework for Hardware-aware Neural Architecture Search
Recent advancements in Artificial Intelligence (AI), driven by Neural Networks (NN), demand innovative neural architecture designs, particularly within the constrained environments of Internet of Things (IoT) systems, to…
Evolutionary AlgorithmsHardware Aware Neural Architecture SearchNeural Architecture SearchInfluence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the firs…
Evolutionary AlgorithmsA Theoretical Assessment of Solution Quality in Evolutionary Algorithms for the Knapsack Problem
Evolutionary algorithms are well suited for solving the knapsack problem. Some empirical studies claim that evolutionary algorithms can produce good solutions to the 0-1 knapsack problem. Nonetheless, few rigorous invest…
Evolutionary AlgorithmsFixation in large populations: a continuous view of a discrete problem
We study fixation in large, but finite, populations with two types, and dynamics governed by birth-death processes. By considering a restricted class of such processes, we derive a continuous approximation for the probab…
validOffline Multi-Task Multi-Objective Data-Driven Evolutionary Algorithm with Language Surrogate Model and Implicit Q-Learning
Data-driven evolutionary algorithms has shown surprising results in addressing expensive optimization problems through robust surrogate modeling. Though promising, existing surrogate modeling schemes may encounter limita…