This paper presents an extensive literature review on Binary Neural Network (BNN). BNN
utilizes binary weights and activation function parameters to substitute the full-precision …

Integrated photonic circuits are created as a stable and small form factor analogue of fiber-
based optical systems, from wavelength-division multiplication transceivers to more recent …

Modern deep neural networks (DNNs) require billions of multiply-accumulate operations per
inference. Given that these computations demand relatively low precision, it is feasible to …

Inference at-the-edge using embedded machine learning models is associated with
challenging trade-offs between resource metrics, such as energy and memory footprint, and …

Recently, on-device training has become crucial for the success of edge intelligence.
However, frequent data movement between computing units and memory during training …

For state-of-the-art artificial intelligence (AI) accelerators, there have been large advances in
both all-digital and analog/mixed-signal circuit-based designs. This article presents a …

Quantum computing is poised to solve practically useful problems which are computationally
intractable for classical supercomputers. However, the current generation of quantum …

Machine learning (ML) models can be trade secrets due to their development cost. Hence,
they need protection against malicious forms of reverse engineering (eg, in IP piracy). With a …

Intellectual Property (IP) thefts of trained machine learning (ML) models through side-
channel attacks on inference engines are becoming a major threat. Indeed, several recent …

An eight-core 64-b processor extends RISC-V to perform multiply–accumulate (MAC) within
the shared last level cache (LLC). Instead of moving data from the LLC to the core, compute …