FPGA HLS today: successes, challenges, and opportunities
The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototyping to deployment. A decade later, in this article, we assess the progress …
went from prototyping to deployment. A decade later, in this article, we assess the progress …
hls4ml: An open-source codesign workflow to empower scientific low-power machine learning devices
F Fahim, B Hawks, C Herwig, J Hirschauer… - arXiv preprint arXiv …, 2021 - arxiv.org
Accessible machine learning algorithms, software, and diagnostic tools for energy-efficient
devices and systems are extremely valuable across a broad range of application domains …
devices and systems are extremely valuable across a broad range of application domains …
Cosa: Scheduling by constrained optimization for spatial accelerators
Recent advances in Deep Neural Networks (DNNs) have led to active development of
specialized DNN accelerators, many of which feature a large number of processing …
specialized DNN accelerators, many of which feature a large number of processing …
Agile SoC development with open ESP
ESP is an open-source research platform for heterogeneous SoC design. The platform
combines a modular tile-based architecture with a variety of application-oriented flows for …
combines a modular tile-based architecture with a variety of application-oriented flows for …
High-level synthesis design space exploration: Past, present, and future
BC Schafer, Z Wang - … on Computer-Aided Design of Integrated …, 2019 - ieeexplore.ieee.org
This article presents a survey of the different modern high-level synthesis (HLS) design
space exploration (DSE) techniques that have been proposed so far to automatically …
space exploration (DSE) techniques that have been proposed so far to automatically …
GRANNITE: Graph neural network inference for transferable power estimation
This paper introduces GRANNITE, a GPU-accelerated novel graph neural network (GNN)
model for fast, accurate, and transferable vector-based average power estimation. During …
model for fast, accurate, and transferable vector-based average power estimation. During …
Magnet: A modular accelerator generator for neural networks
Deep neural networks have been adopted in a wide range of application domains, leading
to high demand for inference accelerators. However, the high cost associated with ASIC …
to high demand for inference accelerators. However, the high cost associated with ASIC …
Gpt4aigchip: Towards next-generation ai accelerator design automation via large language models
The remarkable capabilities and intricate nature of Artificial Intelligence (AI) have
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …
dramatically escalated the imperative for specialized AI accelerators. Nonetheless …
A 0.32–128 TOPS, scalable multi-chip-module-based deep neural network inference accelerator with ground-referenced signaling in 16 nm
Custom accelerators improve the energy efficiency, area efficiency, and performance of
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …
deep neural network (DNN) inference. This article presents a scalable DNN accelerator …
Ultra-elastic cgras for irregular loop specialization
Reconfigurable accelerator fabrics, including coarse-grain reconfigurable arrays (CGRAs),
have experienced a resurgence in interest because they allow fast-paced software algorithm …
have experienced a resurgence in interest because they allow fast-paced software algorithm …