Compute-in-memory (CIM) is a new computing paradigm that addresses the memory-wall
problem in hardware accelerator design for deep learning. The input vector and weight …

When applied to artificial intelligence edge devices, the conventionally von Neumann
computing architecture imposes numerous challenges (eg, improving the energy efficiency) …

From the cloud to edge devices, artificial intelligence (AI) and machine learning (ML) are
widely used in many cognitive tasks, such as image classification and speech recognition. In …

We present XNOR-SRAM, a mixed-signal in-memory computing (IMC) SRAM macro that
computes ternary-XNOR-and-accumulate (XAC) operations in binary/ternary deep neural …

This article presents C3SRAM, an in-memory-computing SRAM macro. The macro is an
SRAM module with the circuits embedded in bitcells and peripherals to perform hardware …

Compute-in-memory (CIM) parallelizes multiply-and-average (MAV) computations and
reduces off-chip weight access to reduce energy consumption and latency, specifically for Al …

Nonvolatile computing-in-memory (nvCIM) can improve the latency (t AC) and energy-
efficiency (EF MAC) of tiny AI edge devices performing multiply-and-accumulate (MAC) …

This article (Colonnade) presents a fully digital bit-serial compute-in-memory (CIM) macro.
The digital CIM macro is designed for processing neural networks with reconfigurable 1-16 …

Recent SRAM-based computation-in-memory (CIM) macros enable mid-to-high precision
multiply-and-accumulate (MAC) operations with improved energy efficiency using ultra …

Advanced AI edge chips require multibit input (IN), weight (W), and output (OUT) for CNN
multiply-and-accumulate (MAC) operations to achieve an inference accuracy that is …