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FPGA HLS today: successes, challenges, and opportunities
The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …
went from prototy** to deployment. A decade later, in this article, we assess the progress …
Pytorch 2: Faster machine learning through dynamic python bytecode transformation and graph compilation
This paper introduces two extensions to the popular PyTorch machine learning framework,
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …
TorchDynamo and TorchInductor, which implement the torch. compile feature released in …
Pathways: Asynchronous distributed dataflow for ml
We present the design of a new large scale orchestration layer for accelerators. Our system,
Pathways, is explicitly designed to enable exploration of new systems and ML research …
Pathways, is explicitly designed to enable exploration of new systems and ML research …
Tensorir: An abstraction for automatic tensorized program optimization
Deploying deep learning models on various devices has become an important topic. The
wave of hardware specialization brings a diverse set of acceleration primitives for multi …
wave of hardware specialization brings a diverse set of acceleration primitives for multi …
A survey on deep learning hardware accelerators for heterogeneous hpc platforms
Recent trends in deep learning (DL) imposed hardware accelerators as the most viable
solution for several classes of high-performance computing (HPC) applications such as …
solution for several classes of high-performance computing (HPC) applications such as …
Challenges and opportunities to enable large-scale computing via heterogeneous chiplets
Fast-evolving artificial intelligence (AI) algorithms such as large language models have
been driving the ever-increasing computing demands in today's data centers …
been driving the ever-increasing computing demands in today's data centers …
Allo: A programming model for composable accelerator design
Special-purpose hardware accelerators are increasingly pivotal for sustaining performance
improvements in emerging applications, especially as the benefits of technology scaling …
improvements in emerging applications, especially as the benefits of technology scaling …
{SecretFlow-SPU}: A performant and {User-Friendly} framework for {Privacy-Preserving} machine learning
With the increasing public attention to data security and privacy protection, privacy-
preserving machine learning (PPML) has become a research hotspot in recent years …
preserving machine learning (PPML) has become a research hotspot in recent years …
Apollo: Automatic partition-based operator fusion through layer by layer optimization
We study fusion for deep neural networks (DNNs) in a just-in-time (JIT) compilation
framework Apollo. It considers both memory-and compute-bound tensor operators for fusion …
framework Apollo. It considers both memory-and compute-bound tensor operators for fusion …
AKG: automatic kernel generation for neural processing units using polyhedral transformations
Existing tensor compilers have proven their effectiveness in deploying deep neural networks
on general-purpose hardware like CPU and GPU, but optimizing for neural processing units …
on general-purpose hardware like CPU and GPU, but optimizing for neural processing units …