A survey on deep learning hardware accelerators for heterogeneous hpc platforms

C Silvano, D Ielmini, F Ferrandi, L Fiorin… - arXiv preprint arXiv …, 2023 - arxiv.org
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 …

Allo: A Programming Model for Composable Accelerator Design

H Chen, N Zhang, S Xiang, Z Zeng, M Dai… - Proceedings of the ACM …, 2024 - dl.acm.org
Special-purpose hardware accelerators are increasingly pivotal for sustaining performance
improvements in emerging applications, especially as the benefits of technology scaling …

From cnn to dnn hardware accelerators: A survey on design, exploration, simulation, and frameworks

LR Juracy, R Garibotti, FG Moraes - Foundations and Trends® …, 2023 - nowpublishers.com
Over the past decade, a massive proliferation of machine learning algorithms has emerged,
from applications for surveillance to self-driving cars. The turning point occurred with the …

An open-source and extensible framework for fast prototyping and benchmarking of spiking neural network hardware

S Matinizadeh, A Das - 2024 34th International Conference on …, 2024 - ieeexplore.ieee.org
Spiking neural networks (SNNs) are bioplausible machine learning models that use discrete
spikes to encode, compute, and transmit information. Combined with event-driven low …

AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators

NB Agostini, J Haris, P Gibson… - 2024 IEEE/ACM …, 2024 - ieeexplore.ieee.org
This paper addresses the need for automatic and efficient generation of host driver code for
arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an important …

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

F Ferrandi, S Curzel, L Fiorin, D Ielmini… - arXiv preprint arXiv …, 2023 - arxiv.org
In recent years, the field of Deep Learning has seen many disruptive and impactful
advancements. Given the increasing complexity of deep neural networks, the need for …

Analyzing inference workloads for spatiotemporal modeling

M Jain, NB Agostini, S Ghosh, A Tumeo - Future Generation Computer …, 2025 - Elsevier
Ensuring power grid resiliency, forecasting climate conditions, and optimization of
transportation infrastructure are some of the many application areas where data is collected …

OpenHLS: high-level synthesis for low-latency deep neural networks for experimental science

M Levental, A Khan, R Chard, K Yoshii, K Chard… - arXiv preprint arXiv …, 2023 - arxiv.org
In many experiment-driven scientific domains, such as high-energy physics, material
science, and cosmology, high data rate experiments impose hard constraints on data …

Model-Based FPGA Implementation of a 6-DoF Dynamical Model Accelerator

S Memis, R Yeniceri - IEEE Access, 2024 - ieeexplore.ieee.org
The mathematical model of 6-DoF dynamics is used in different applications. In general,
software-based solutions are utilized to implement the 6-DoF dynamic model. This paper …

The support of mlir hls adaptor for llvm ir

GM Liang, CY Yuan, MS Yu, TL Chen… - … Proceedings of the …, 2022 - dl.acm.org
Since the emergence of MLIR, High-level Synthesis (HLS) tools started to design in multi-
level abstractions. Unlike the traditional HLS tools that are based on a single abstraction (eg …