acceleration opportunities, including with accelerators for machine learning operations, an end-to-end application performance analysis has not been well studied, particularly for data analytics and model scoring pipelines. In this paper, we study speedups and overheads of using ...
This paper presents an approach to enhance the performance of machine learning applications based on hardware acceleration. This approach is based on parameterised architectures designed for Convolutional Neural Network (CNN) and Support Vector Machine (SVM), and the associated design flow common to both...
Woehrle, Hendrik & Frank Kirchner (2015), Reconfigurable Hardware-Based Accelera- tion for Machine Learning and Signal Processing, 1sta edicao, Springer Fachmedien Wiesbaden, Wiesbaden.H. Woehrle, F. Kirchner, Reconfigurable hardware-based acceleration for machine learning and signal processing, Springer...
Optionally, you can enable hardware acceleration for machine learning and transcoding. See the [Hardware Transcoding](/docs/features/hardware-transcoding.md) and [Hardware-Accelerated Machine Learning](/docs/features/ml-hardware-acceleration.md) guides for info on how to set these up. ::: ### St...
Source code for ourASPLOS 2023paper, "MiniMalloc: A Lightweight Memory Allocator for Hardware-Accelerated Machine Learning." Overview An increasing number of deep learning workloads are being supported byhardware acceleration. In order to unlock the maximum performance of a hardware accelerator, a mac...
PhD Project - Hardware Acceleration for Deep Learning at University of Sheffield, listed on FindAPhD.com
extensions for frameworks today, such as Intel Extension for PyTorch*, Intel® Extension for TensorFlow* and Intel® Extension for DeepSpeed*. By using these extensions together with the upstream framework releases, users will be able to realize drop-in acceleration for machine learning workflows....
What type of GPU (video card) is best for machine learning and AI? NVIDIA dominates for GPU compute acceleration, and is unquestionably the standard. Their GPUs will be the most supported and easiest to work with. There are other accelerators such as a few of the high-end AMD GPUs, ...
Ted Way, from the Azure Machine Learning team, joins Olivier Bloch on The IoT Show to discuss Hardware Acceleration for AI at the Edge. We discuss scenarios and technologies Microsoft develops and uses to accelerate AI in the Cloud and at the Edge, including GPUs, FPGA, CPU,...
DirectML is a high-performance, hardware-accelerated DirectX 12 library for machine learning. DirectML provides GPU acceleration for common machine learning tasks across a broad range of supported hardware and drivers, including all DirectX 12-capable GPUs from vendors such as AMD, Intel, NVIDIA, and...