TL;DR
This paper introduces a methodology, based on Negation-Permutation-Negation (NPN) classes, to explore a reduced number of approximate multipliers based on the error within DNN applications to overcome limitations of previous works.
E-ALS is proposed, an e-graph-based framework for approximation-aware structural search that combines Function-Reduced Saturation, an ALS-coupled surrogate, search-based extraction, and budget-guided refinement to identify approximation-friendly equivalent structures.
The platform exploits the reconfigurable logic of Field-Programmable Gate Arrays to implement approximate multipliers directly in hardware, eliminating the need for LUT-based emulation on CPU/GPU platforms and thereby enabling efficient DNN inference while significantly reducing evaluation time on large datasets.
CircuitsDNA is introduced, an evolutionary framework that automatically evolves accuracy-configurable arithmetic circuits supporting multiple accuracy modes within a single circuit that eliminates all search stalls observed in conventional methods across 8/12/16-bit multipliers, while adaptive mutation provides up to 1...
Quantized Neural Networks are widely used in safety-critical systems due to their efficiency, yet they remain vulnerable to adversarial perturbations. Existing verification approaches based on the Big-<inline-formula> <tex-math notation="LaTeX">$M$ </tex-math></inline-formula> Mixed-Integer Linear Programming (MILP) su...
Recently, approximate computing has become an active topic of interest as a state of the art method to enhance the efficiency in both performance and energy consumption of the arithmetic circuits in error-tolerant applications. Numerous multimedia, image processing, and machine learning systems today can accept some sm...
Federated learning (FL) enables clients to collaboratively train a global model by sharing gradient updates instead of raw data. However, recent studies show that these shared gradients can be exploited to reconstruct private training data through gradient inversion attacks, posing a serious threat to client privacy. A...
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