Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.
DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.
Approximate computing is an emerging paradigm for designing error-resilient applications. It reduces circuit area, power, and delay at the cost of allowingintroducing errors. This paper introducesproposes a powerful technique, termed Approximate Resubstitution (AppResub), to approximately simplify the circuit. AppResub re-expressesreplaces a node's function with a simpler approximate function onusing existing nodes in the circuit, by replacing it with a simpler approximate function to reduce the hardware cost. Leveraging AppResub, an efficient flow for approximate logic synthesis (ALS) is developed by iteratively applying a set of promising AppResubs for circuit simplification. To evaluate errors caused by a set of AppResubs, a novel error model capable of efficiently computing an error upper bound is used to smartly apply AppResubs in the ALS flow. The experimental results demonstrate that, compared to a state-of-the-art method, the proposed flow further reduces 20.9% area and 21.7% delay under the mean error distance constraint, making itwhile being 400× faster. The code of our flow is open-source.