AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
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.
Approximate logic synthesis (ALS) improves circuit power, performance, and area by trading exact correctness for bounded functional error. However, existing structural ALS methods largely overlook structural bias: even functionally equivalent netlists can expose markedly different approximation opportunities and lead t...
Approximate multipliers can reduce hardware area and energy consumption in Deep Neural Network (DNN) inference; however, they introduce computational errors. Assessing the accuracy of numerous approximate multiplier designs across diverse DNN models and large-scale datasets remains challenging due to prohibitive evalua...
Emerging edge AI workloads increasingly require arithmetic units that can trade computational accuracy for efficiency on demand. However, existing approximate arithmetic circuits are typically fixed-accuracy or rely on predefined structures for runtime configurability. This work introduces CircuitsDNA, an evolutionary...
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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