Computing in memory (CIM) could be used to overcome the von Neumann bottleneck and to
provide sustainable improvements in computing throughput and energy efficiency …

Deep neural networks offer considerable potential across a range of applications, from
advanced manufacturing to autonomous cars. A clear trend in deep neural networks is the …

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

Silicon-based static random access memories (SRAM) and digital Boolean logic have been
the workhorse of the state-of-the-art computing platforms. Despite tremendous strides in …

We present a 256 256 in-memory compute (IMC) core designed and fabricated in 14-nm
CMOS technology with backend-integrated multi-level phase change memory (PCM). It …

Neuromorphic engineering, a methodology for emulating synaptic functions or neural
systems, has attracted tremendous attention for achieving next-generation artificial …

In-memory computing'is being widely explored as a novel computing paradigm to mitigate
the well known memory bottleneck. This emerging paradigm aims at embedding some …

Traditional computing systems based on the von Neumann architecture are fundamentally
bottlenecked by data transfers between processors and memory. The emergence of data …

In-memory computing architectures present a promising solution to address the memoryand
the power-wall challenges by mitigating the bottleneck between processing units and …

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