Currently, Machine Learning (ML) is becoming ubiquitous in everyday life. Deep Learning
(DL) is already present in many applications ranging from computer vision for medicine to …

DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

A cost-effective multi-tenant neural network execution is becoming one of the most important
design goals for modern neural network accelerators. For example, as emerging AI services …

The success of DNN pruning has led to the development of energy-efficient inference
accelerators that support pruned models with sparse weight and activation tensors. Because …

We present a method for transparently identifying ineffectual computations during inference
with Deep Learning models. Specifically, by decomposing multiplications down to the bit …

Advances in deep learning and neural networks have resulted in rapid development of
hardware accelerators that support them. A large majority of ASIC accelerators, however …

With reduced data reuse and parallelism, recent convolutional neural networks (CNNs)
create new challenges for FPGA acceleration. Systolic arrays (SAs) are efficient, scalable …

The current trend for deep learning has come with an enormous computational need for
billions of Multiply-Accumulate (MAC) operations per inference. Fortunately, reduced …

Dataflow accelerators feature simplicity, programmability, and energy-efficiency and are
visualized as a promising architecture for accelerating perfectly nested loops that dominate …