In recent years, it has been witnessed that the systolic array is a successful architecture for
DNN hardware accelerators. However, the design of systolic arrays also encountered many …

Transformer model is becoming prevalent in various AI applications with its outstanding
performance. However, the high cost of computation and memory footprint make its …

Due to complex interactions among various deep neural network (DNN) optimization
techniques, modern DNNs can have weights and activations that are dense or sparse with …

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 …

N: M sparsity is an emerging model compression method supported by more and more
accelerators to speed up sparse matrix multiplication in deep neural networks. Most existing …

Deep Learning (DL) acceleration support in CPUs has recently gained a lot of traction, with
several companies (Arm, Intel, IBM) announcing products with specialized matrix engines …

This paper proposes RM-STC, a novel GPU tensor core architecture designed for sparse
Deep Neural Networks (DNNs) with two key innovations:(1) native support for both training …

Gigantic rates of data production in the era of Big Data, Internet of Thing (IoT), and Smart
Cyber Physical Systems (CPS) pose incessantly escalating demands for massive data …

DNN pruning is a popular way to reduce the size of a model, improve the inferencelatency,
and minimize the power consumption on DNN accelerators. However, existing approaches …

Tensor-train (TT) decomposition enables ultra-high compression ratio, making the deep
neural network (DNN) accelerators based on this method very attractive. TIE, the state-of-the …