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 …

Tensor computations overwhelm traditional general-purpose computing devices due to the
large amounts of data and operations of the computations. They call for a holistic solution …

Nowadays, artificial intelligence (AI) technologies, especially deep neural networks (DNNs),
play an vital role in solving many problems in both academia and industry. In order to …

Federated learning (FL) allows participants to train deep learning models collaboratively
without disclosing their data to the server or any other participants, providing excellent value …

This paper presents a mixed-signal SRAM-based in-memory computing (IMC) macro for
processing binarized neural networks. The IMC macro consists of (16K) SRAM-based …

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise,
making aggressive voltage scaling attractive as a power-saving technique for both logic and …

Multiple-valued logic (MVL) shows considerable advantages over binary logic in certain
applications because of the increased informational content of its signals, and hence …

As convolutional neural networks (CNNs) become more and more diverse and complicated,
acceleration of CNNs increasingly encounters a bottleneck of balancing performance …

In this article, a quantized network acceleration processor (QNAP) is proposed to efficiently
accelerate CNN processing by eliminating most unessential operations based on algorithm …

Energy-efficient processors are crucial for accelerating deep neural networks (DNNs) on
edge devices with limited battery capacity. To reduce energy consumption, time-domain …