Deep learning has become ubiquitous, touching daily lives across the globe. Today,
traditional computer architectures are stressed to their limits in efficiently executing the …

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 …

Computing systems are widely used in medical diagnosis, climate prediction, autonomous
vehicles, etc. As the key part of electronics, the performance of computing systems is crucial …

The rapid growth of CMOS logic circuits has surpassed the advancements in memory
access, leading to significant “memory wall” bottlenecks, particularly in artificial intelligence …

The major challenge faced by modern compute-in-memory (CIM) designs is that they rely
heavily on mixed-signal data converters such as digital-to-analog converters (DACs) and …

In-memory computing for multiplication operations is an approach towards mitigating the
overhead caused by the migration of data between the memory and the processor observed …

Applications of federated learning involve devices with extremely limited computational
resources and often with considerable heterogeneity in terms of energy efficiency, latency …

The shortcomings of traditional von Neumann architectures have become more evident in
the artificial intelligence (AI) and machine learning (ML) era. The continuous data flow …

This paper presents a novel compute-in-memory (CIM) macro, which mainly consists of
three modules: input generator, 9T1C-SRAM CIM array and charge-integration-count output …

This article demonstrates the first functional neuromorphic spiking neural network (SNN) that
processes the time-to-first-spike (TTFS) encoded analog spiking signals with the second …