Biological neural networks continue to inspire new developments in algorithms and
microelectronic hardware to solve challenging data processing and classification problems …

Recent review papers have investigated seizure prediction, creating the possibility of
preempting epileptic seizures. Correct seizure prediction can significantly improve the …

This paper presents a machine-learning classifier where computations are performed in a
standard 6T SRAM array, which stores the machine-learning model. Peripheral circuits …

Analog hardware accelerators, which perform computation within a dense memory array,
have the potential to overcome the major bottlenecks faced by digital hardware for data …

A multi-functional in-memory inference processor integrated circuit (IC) in a 65-nm CMOS
process is presented. The prototype employs a deep in-memory architecture (DIMA), which …

Advances in algorithms and low-power computing hardware imply that machine learning is
of potential use in off-grid medical data classification and diagnosis applications such as …

A 16-channel noninvasive closed-loop beginning-and end-of-seizure detection SoC is
presented. The dual-channel charge recycled (DCCR) analog front end (AFE) achieves …

In this paper we propose a novel energy efficient approach for the recognition of human
activities using smartphones as wearable sensing devices, targeting assisted living …

In this paper, we propose the concept of compute memory, where computation is deeply
embedded into the memory (SRAM). This deep embedding enables multi-row read access …

Deep neural networks (DNNs) have attracted substantial interest in recent years due to their
superior performance on many classification and regression tasks as compared to other …