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 …
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 …
From cnn to dnn hardware accelerators: A survey on design, exploration, simulation, and frameworks
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 …
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 …
spikes to encode, compute, and transmit information. Combined with event-driven low …
AXI4MLIR: User-Driven Automatic Host Code Generation for Custom AXI-Based Accelerators
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 …
arbitrary custom AXI-based accelerators targeting linear algebra algorithms, an important …
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
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 …
advancements. Given the increasing complexity of deep neural networks, the need for …
Analyzing inference workloads for spatiotemporal modeling
Ensuring power grid resiliency, forecasting climate conditions, and optimization of
transportation infrastructure are some of the many application areas where data is collected …
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
In many experiment-driven scientific domains, such as high-energy physics, material
science, and cosmology, high data rate experiments impose hard constraints on data …
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 …
software-based solutions are utilized to implement the 6-DoF dynamic model. This paper …
The support of mlir hls adaptor for llvm ir
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 …
level abstractions. Unlike the traditional HLS tools that are based on a single abstraction (eg …