Interactive massively parallel computations are critical for machine learning and data
analysis. These computations are a staple of the MIT Lincoln Laboratory Supercomputing …

Serverless or functions as a service runtimes have shown significant benefits to efficiency
and cost for event-driven cloud applications. Although serverless runtimes are limited to …

Machine learning sits at the core of many essential products and services at Facebook. This
paper describes the hardware and software infrastructure that supports machine learning at …

In this paper, we introduce the Tensor Streaming Processor (TSP) architecture, a functionally-
sliced microarchitecture with memory units interleaved with vector and matrix deep learning …

There is a growing interest in serverless compute, a cloud computing model that automates
infrastructure resource-allocation and management while billing customers only for the …

We present FireSim, an open-source simulation platform that enables cycle-exact
microarchitectural simulation of large scale-out clusters by combining FPGA-accelerated …

Deep neural networks have recently gained tremendous interest due to their capabilities in a
wide variety of application areas such as computer vision and speech recognition. Thus it is …

The performance of Deep-Learning (DL) computing frameworks rely on the performance of
data ingestion and checkpointing. In fact, during the training, a considerable high number of …

The Microarchitecture of Tesla's ExaScale Computer Emil Talpes, Douglas Williams,
DebjitDas Sarma2022 IEEE Hot Chips 34 Symposium (HCS)| 978-1-6654-6028 …

For decades, the growth and volume of digital data collection has made it challenging to
digest large volumes of information and extract underlying structure. CoinedBig Data' …