In artificial intelligence, the large role is played by machine learning (ML) in a variety of applications. This article aims at providing a comprehensive survey on summarizing recent trends and advances in hardware accelerator design for machine learning based on various hardware platforms like ASIC...
First, we perform local search to determine the architecture for each pipeline and tensor model stage. Specifically, the system iteratively generates architectural configurations and tunes the design using a novel heuristic-based approach that prioritizes accelerator resour...
Hardware acceleratorEdge computingMatrix multiplicationApproximate computing has emerged as an efficient design methodology for improving the performance and power-efficiency of digital systems by allowing a negligible loss in the output accuracy. Dedicated hardware accelerators built using approximate circuits ...
Deep learning. Nature 521, 436–444. https://doi.org/10.1038/nature14539 (2015). Article ADS CAS PubMed Google Scholar Chen, Y., Krishna, T., Emer, J. S. & Sze, V. Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks. IEEE J. Solid-State ...
An Artificial Intelligence accelerator is a type of specialized hardware accelerator designed to accelerate Artificial Intelligence-based applications, especially deep neural networks and machine learning. Quantization Quantization is the process of constraining data Hyunbin Park received his B.S. degree in ...
Dedicated hardware accelerators built using approximate circuits can solve power-performance trade-off in the computationally complex applications like deep learning. This paper proposes an approximate radix-4 Booth multiplier and hardware accelerator for deploying deep learning applications on power-restricted...
This webinar details approaches to integrating accelerator blocks into processor-based sub-systems, interfacing to software, and verifying the accelerator in the context of the larger system. It also covers deploying the system onto a FPGA prototyping board. Leveraging HLS IP to Accelerate Design and...
那么为什么能叫glow,glow其实是graph+lowering的简写,意思就是说,通过这个低阶的IR,从而在针对大量不同的上层model中的op到下层不同hardware accelerator的实现都尽可能通过一些比较简单的线性代数源语来实现,有点类似精简指令集的感觉。 为什么要做deep learning compiler,其实motivation很简单。我们现在经常用的深度学习...
programmable accelerator: 可以适应新的算法,但是需要强大的可以将大量工作映射入有限的硬件函数的深度学习编译器 贡献 使用了两级的指令集架构 high-level ISA:允许编译器栈显式的进行任务调度 low-level ISA:提供软件定义的操作灵活性 VTA框架是完全可参数化的(parameterizable),也就是说各个组件都可以更换成适应dl...
Eyeriss: A spatial architecture for energy-efficient dataflow for convolutional neural networks[C]. ACM SIGARCH Computer Architecture News. IEEE Press, 2016, 44(3): 367-379. Chen Y H, Krishna T, Emer J S, et al. Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional ...