Achieving high performance for compute-intensive operators in machine learning (ML)
workloads is a crucial but challenging task. Many ML and system practitioners rely on …

Deep Learning (DL) models have achieved superior performance. Meanwhile, computing
hardware like NVIDIA GPUs also demonstrated strong computing scaling trends with 2x …

We present a new class of fast polylog-linear algorithms based on the theory of structured
matrices (in particular low displacement rank) for integrating tensor fields defined on …

Graphic Processing Units (GPUs) are gradually becoming mainstream computing resource
for efficient execution of applications both on-premises and in the cloud. Currently however …

This paper investigates how to efficiently deploy vision transformers on edge devices for
small workloads. Recent methods reduce the latency of transformer neural networks by …

Channel pruning has recently become a widely used model compression method. However,
most existing channel pruning methods only prune to decrease the model size, such as the …

Graphic Processing Units (GPUs) are known for the large computing capabilities they offer
users compared to traditional CPUs. However, the issue of resource under-utilization is …

We present nnPerf, a real-time on-device profiler designed to collect and analyze the DNN
model run-time inference latency on mobile platforms. nnPerf demystifies the hidden layers …

Channel pruning is one of the mainly used meth-ods in current network model compression.
However, existing channel pruning methods lack effective hardware runtime latency …

Deep neural networks (DNNs) are the current state-of-the-art machine learning algorithms in
various application domains. Due to their importance, it is crucial that we guarantee their …