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Gshard: Scaling giant models with conditional computation and automatic sharding
D Lepikhin, HJ Lee, Y Xu, D Chen, O Firat… - ar** high-performance sparse operators can be difficult and …
Multi-task temporal shift attention networks for on-device contactless vitals measurement
Telehealth and remote health monitoring have become increasingly important during the
SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on …
SARS-CoV-2 pandemic and it is widely expected that this will have a lasting impact on …
A hardware–software blueprint for flexible deep learning specialization
This article describes the Versatile Tensor Accelerator (VTA), a programmable DL
architecture designed to be extensible in the face of evolving workloads. VTA achieves …
architecture designed to be extensible in the face of evolving workloads. VTA achieves …
Getting to the point: index sets and parallelism-preserving autodiff for pointful array programming
We present a novel programming language design that attempts to combine the clarity and
safety of high-level functional languages with the efficiency and parallelism of low-level …
safety of high-level functional languages with the efficiency and parallelism of low-level …
Graph IRs for impure higher-order languages: Making aggressive optimizations affordable with precise effect dependencies
Graph-based intermediate representations (IRs) are widely used for powerful compiler
optimizations, either interprocedurally in pure functional languages, or intraprocedurally in …
optimizations, either interprocedurally in pure functional languages, or intraprocedurally in …
Demystifying differentiable programming: Shift/reset the penultimate backpropagator
Deep learning has seen tremendous success over the past decade in computer vision,
machine translation, and gameplay. This success rests crucially on gradient-descent …
machine translation, and gameplay. This success rests crucially on gradient-descent …
Zero bubble pipeline parallelism
Pipeline parallelism is one of the key components for large-scale distributed training, yet its
efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we …
efficiency suffers from pipeline bubbles which were deemed inevitable. In this work, we …
A tensor compiler with automatic data packing for simple and efficient fully homomorphic encryption
Fully Homomorphic Encryption (FHE) enables computing on encrypted data, letting clients
securely offload computation to untrusted servers. While enticing, FHE has two key …
securely offload computation to untrusted servers. While enticing, FHE has two key …