Emerging edge computing platforms often contain machine learning (ML) accelerators that
can accelerate inference for a wide range of neural network (NN) models. These models are …

Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating
platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units …

Dense matrix multiply (MM) serves as one of the most heavily used kernels in deep learning
applications. To cope with the high computation demands of these applications …

Real-time multi-task multi-model (MTMM) workloads, a new form of deep learning inference
workloads, are emerging for applications areas like extended reality (XR) to support …

Driven by the wide adoption of deep neural networks (DNNs) across different application
domains, multi-tenancy execution, where multiple DNNs are deployed simultaneously on …

XR, consisting of Virtual Reality (VR) and Augmented Reality (AR) together, will be the next
general computing platform, dominating our relationship with the digital world for the next 50 …

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 …

With the increasing size of DNN models and the growing discrepancy between compute
performance and memory bandwidth, fusing multiple layers together to reduce off-chip …

Deep neural networks (DNNs) have made significant achievements in a wide variety of
domains. For the deep learning tasks, multiple excellent hardware platforms provide efficient …

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 …