As von Neumann computing architectures become increasingly constrained by data-
movement overheads, researchers have started exploring in-memory computing (IMC) …

Microprocessors use static random-access memory (SRAM) cells in the cache memory
design. As a part of the central computing component, their performance is critical. Modern …

The proliferation of data has facilitated global accessibility, which demands escalating
amounts of power for data storage and processing purposes. In recent years, there has been …

Artificial intelligence (AI) processes data-centric applications with minimal effort. However, it
poses new challenges to system design in terms of computational speed and energy …

Neuromorphic vision sensors (NVSs) can enable energy savings due to their event-driven
that exploits the temporal redundancy in video streams from a stationary camera. However …

In-cache computing technology transforms existing caches into long-vector compute units
and offers low-cost alternatives to building expensive vector engines for mobile CPUs …

Neuromorphic vision sensors (NVS) 1 are key enablers in traffic monitoring and surveillance
systems that exploit the temporal redundancy in video streams to get \gt2X energy savings …

Microprocessors use static random-access memory (SRAM) cells in the cache memory
design. As a part of the central computing component, their performance is critical. Modern …

Due to high data acquisition rates and large data volumes, on-board hyperspectral imagery
(HSI) demands data compression before transmission. The major bottleneck in on-board …

In this paper, we propose a floating-gate-based synaptic transistor with two independent
control gates that implement both offline and online learning. Unlike previous research on …