The difficulty of deploying various deep learning (DL) models on diverse DL hardware has
boosted the research and development of DL compilers in the community. Several DL …

Recent years have seen a phenomenal rise in the performance and applications of
transformer neural networks. The family of transformer networks, including Bidirectional …

Google deployed several TPU generations since 2015, teaching us lessons that changed
our views: semi-conductor technology advances unequally; compiler compatibility trumps …

Vision-language models trained with contrastive learning on large-scale noisy data are
becoming increasingly popular for zero-shot recognition problems. In this paper we improve …

A domain-specific supercomputer for training deep neural networks Page 1 JULY 2020 | VOL.
63 | NO. 7 | COMMUNICATIONS OF THE ACM 67 DOI:10.1145/3360307 Google’s TPU …

This book provides a structured treatment of the key principles and techniques for enabling
efficient processing of deep neural networks (DNNs). DNNs are currently widely used for …

Abstract Large Language Models (LLMs) have demonstrated remarkable capabilities in
important tasks such as natural language understanding, language generation, and …

In this paper, we explore the limits of Microsoft Floating Point (MSFP), a new class of
datatypes developed for production cloud-scale inferencing on custom hardware. Through …

We present new benchmarks on evaluation code generation models: MBXP and Multilingual
HumanEval, and MathQA-X. These datasets cover over 10 programming languages and are …

With advances in deep-neural-network applications the increasingly large data movement
through memory channels is becoming inevitable: specifically, RNN and MLP applications …