In-memory computing (IMC) refers to non-von Neumann architectures where data are
processed in situ within the memory by taking advantage of physical laws. Among the …

Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well
suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory …

Ising machines, which are hardware implementations of the Ising model of coupled spins,
have been influential in the development of unsupervised learning algorithms at the origins …

The increasing use of domain-specific computing hardware and architectures has led to an
increasing demand for unconventional computing approaches. One such approach is the …

Energy efficiency continues to be the core design challenge for artificial intelligence (AI)
hardware designers. In this paper, we propose a new AI hardware architecture targeting …

Crossbars of resistive memories, or memristors, provide a road to reduce the energy
consumption of artificial neural networks, by naturally implementing multiply accumulate …

Stochastic computing (SC) is an alternative computing domain for ubiquitous deterministic
computing whereby a single logic gate can perform the arithmetic operation by exploiting the …

This paper proposes an in-memory binary spiking neural network (BSNN) based on spin-
transfer-torque magnetoresistive RAM (STT-MRAM). We propose residual BSNN learning …

Specialized hardware accelerators beyond von-Neumann, that offer processing capability in
where the data resides without moving it, become inevitable in data-centric computing …

Spiking Neural Networks (SNNs) are considered the third generation of NNs and can reach
similar accuracy as conventional deep NNs, but with a considerable improvement in …