Deep learning models are computationally intense, and implementations often have to be
highly optimized by experts or hardware vendors to be usable in practice. The DL compiler …

The success of machine learning has prospered Machine-Learning-as-a-Service (MLaaS)-
deploying trained machine learning (ML) models in cloud to provide low latency inference …

The proportion of machine learning (ML) inference in modern cloud workloads is rapidly
increasing, and graphic processing units (GPUs) are the most preferred computational …

With the increase in the computation intensity of the chip, the mismatch between
computation layer shapes and the available computation resource significantly limits the …

Deep learning models are increasingly used for end-user applications, supporting both
novel features such as facial recognition, and traditional features, eg web search. To …

Machine learning (ML) has been embedded in many Internet of Things (IoT) applications
(eg, smart home and autonomous driving). Yet it is often infeasible to deploy ML models on …

Despite the fact that GPUs and accelerators are more efficient in deep learning (DL),
commercial clouds like Facebook and Amazon now heavily use CPUs in DL computation …

FPGAs facilitate prototy** and debug, and recently accelerate full-stack simulations due to
their rapid turnaround time (TAT). However, this TAT is restrictive in exhaustive design space …

The goal of semantic code search or text-to-code search is to retrieve a semantically
relevant code snippet from an existing code database using a natural language query …

Abstract Autonomous Vehicles (AVs) have the potential to revolutionize transportation
through their significant safety, environmental and mobility benefits. However, despite their …