There is an emerging need to design configurable accelerators for the high-performance
computing (HPC) and artificial intelligence (AI) applications in different precisions. Thus, the …

Deep neural networks typically have extensive parameters and computational operations.
Pruning and quantization techniques have been widely used to reduce the complexity of …

Block floating point (BFP), an efficient numerical system for deep neural networks (DNNs),
achieves a good trade-off between dynamic range and hardware costs. Specifically, prior …

Deep neural networks (DNNs) have become an enabling component for a myriad of artificial
intelligence applications. DNNs have shown sometimes superior performance, even …

In recent years, PIM-based mixed-signal accelerators have been proposed as energy-and
area-efficient solutions with ultra-high throughput to accelerate DNN computations …

Vertical Federated Learning (VFL) is a solution increasingly used by companies with the
same user group but differing features, enabling them to collaboratively train a machine …

In recent years, processing in memory (PIM) based mixedsignal designs have been
proposed as energy-and area-efficient solutions with ultra high throughput to accelerate …

Generative adversarial networks (GANs) have emerged as a powerful solution for
generating synthetic data when the availability of large, labeled training datasets is limited or …

The choice of data type has a major impact on speed, accuracy, and power consumption of
deep learning accelerators. Quantizing the weights and activations of neural networks to …

The two conventional number systems, namely the floating point and fixed point, are
commonly used in almost all general-purpose DNN engines. While the FLP representation …