The Transformer has been an indispensable staple in deep learning. However, for real-life
applications, it is very challenging to deploy efficient Transformers due to the immense …

In-memory computing is an emerging computing paradigm that overcomes the limitations of
exiting Von-Neumann computing architectures such as the memory-wall bottleneck. In such …

In-memory computing seeks to minimize data movement and alleviate the memory wall by
computing in-situ, in the same place that the data is located. One of the key emerging …

The rapid growth of artificial intelligence and the increasing complexity of neural network
models are driving demand for efficient hardware architectures that can address power …

The bottleneck between the processor and memory is the most significant barrier to the
ongoing development of efficient processing systems. Therefore, a research effort begun to …

Mixed-precision quantization works Neural Networks (NNs) are gaining traction for their
efficient realization on the hardware leading to higher throughput and lower energy. In …

Flash memory, as the core unit of a compute-in-memory (CIM) array, requires multiple
positive and negative (PN) high voltages (HVs) for word lines (WLs) to operate during …

A new system-level simulator, eF 2 lowSim, is proposed to estimate the bit-accurate and
cycle-accurate performance of eFlash compute-in-memory (CIM) accelerators for …

In floating gate compute-in-memory (CIM) chips, due to the gate equivalent capacitance of
the large-scale array and the parasitic capacitance of the long-distance transmission wire, it …

The associative processor (AP) is a processing in-memory (PIM) platform that avoids data
movement between the memory and the processor by running computations directly in the …