Jun 18, 2017 · In this paper, we present a multi-pronged approach to address the challenges in meeting both the throughput and the energy efficiency goals for ...

Applications in domains such as image/video processing, autonomous cars, natural language processing, speech synthesis and recognition, genomics and many others ...

In this paper, we present a multi-pronged approach to address the challenges in meeting both the throughput and the energy efficiency goals for DNN training.

Bibliographic details on Accelerator Design for Deep Learning Training: Extended Abstract: Invited.

In this paper, we present a multi-pronged approach to address the challenges in meeting both the throughput and the energy efficiency goals for DNN training.

INVITED: Accelerator Design for Deep Learning Training: Extended Abstract: Invited. Ankur Agrawal; Chia-Yu Chen; et al. 2017; DAC 2017. A 25Gb/s ADC-based ...

Accelerator Design for Deep Learning Training: Extended Abstract: Invited ... Deep Neural Networks (DNNs) have emerged as a powerful and versatile set of ...

This paper presents a multi-TOPS AI accelerator core for deep learning training and inference. With a programmable architecture and custom ISA, this engine ...

We present a re-configurable accelerator design optimized for CNN-based object-detection applications, especially suitable for mobile FPGA platforms.

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