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

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 …

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 …

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 …

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 article presents an efficient static random access memory (SRAM)-based in-memory
computation (IMC) architecture which is capable of performing image classification with …

The unprecedented growth in Deep Neural Networks (DNN) model size has resulted into a
massive amount of data movement from off-chip memory to on-chip processing cores in …

Artificial intelligence algorithms entail an enormous number of multiplication operations. If
these operations are implemented within a static random-access memory array, the …

Power-efficient data processing subsystems performing millions of complex concurrent
arithmetic operations per second form part of today's essential solution required to meet the …

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