8 part technical HLS video series with real examples applying it to computer vision and deep learning implementation.
DEEP LEARNING ACCELERATORS WITH CONFIGURABLE HARDWARE OPTIONS OPTIMIZABLE VIA COMPILERSystems, devices, and methods related to a Deep Learning Accelerator and memory are described. For example, an integrated circuit device may be configured to execute instructions with matrix operands and configured with ...
In this paper, we present a novel technique to search for hardware architectures of accelerators optimized for end-to-end training of deep neural networks (DNNs). Our approach addresses both single-device and distributed pipeline and tensor model parallel scenarios,...
We designed VTA to expose the most salient and common characteristics of mainstream deep learning accelerators, such as tensor operations, DMA load/stores, and explicit compute/memory arbitration. VTA is more than a standalone accelerator design: it's an end-to-end solution that includes drivers,...
In this context, the primary issues faced by hardware accelerators are loss of accuracy and high power consumption. Introduction Recent progress in deep learning and convolutional neural networks (CNNs) has contributed to the advances in artificial intelligence with respect to tasks such as object ...
深度学习编译器 Compiler for Neural Network hardware accelerators high level op通过low level op(线性代数)来组合,这就是node lowering啊 https://github.com/ybai62868/Glow_example https://github.com/ybai62868/gluon-tutorial - Ewenwan/glow
customizing behavior of these accelerators, even when open sourced, is highly dependent on the availability of a transparent and modular software stack.(transparent 和 modular啥意思?) 运行时系统(runtime system):运行时系统是一种介乎编译(Compile)和解释(Interpret)的运行方式,由编译器(Compiler)首先将源...
Because of increasingly stringent energy constraints (e.g., Dark Silicon, there is a growing consensus in the community that we may be moving towards heterogeneous multi-core architectures, composed of a mix of cores and accelerators. Because our community is traditionally focused on general-purpose...
Optimize with Intel® Gaudi® AI Accelerators Create new deep learning models or migrate existing code in minutes. Deliver Generative AI performance with simplified development and increased productivity. Explore Platform Solutions Intel® Gaudi® AI Accelerator Intel® Data Center GPU Max ...
This property is key to scaling deep neural network accelerators, where increasing the number of processing elements for greater throughput in all-electronic hardware typically implies higher data communication costs due to longer electronic path length. Contrary to other proposed optical neural networks21...