HC29-S7: Neural Net II

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Session 7, Hot Chips 29 (2017), Tuesday, August 22, 2017.

DNN ENGINE: A 16nm Sub-µJ Deep Neural Network Inference Accelerator for the Embedded Masses
Paul Whatmough, Harvard University/ARM Research

DNPU: An Energy-Efficient Deep Neural Network Processor with On-Chip Stereo Matching
Dongjoo Shin and Hoi-Jun Yoo, KAIST

Evaluation of the Tensor Processing Unit: A Deep Neural Network Accelerator for the Datacenter
Cliff Young, Google
  1. hotchipsvideos
  2. neural networks
  3. deep neural networks
  4. neural network inference
  5. DNN Engine
  6. Harvard University
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HC29-S5: FPGAs 2:01:20

HC29-S5: FPGAs

Keynote 13 0:30:01

Keynote 13 IBM AI Accelerating Deep Neural Networks with Analog Nonvolatile Memory Devices

HC29-S1: GPU and 1:37:02

HC29-S1: GPU and Gaming

PowerVR 2NX Neural 0:02:04

PowerVR 2NX Neural Network Accelerator (NNA)

HC30-S8: Machine Learning 1:00:37

HC30-S8: Machine Learning II

HC29-S2: IOT/Embedded 0:55:03

HC29-S2: IOT/Embedded

Tech Talk: Neural 0:09:15

Tech Talk: Neural Net Functions in the Wolfram Language

BDTI Demonstration of 0:02:18

BDTI Demonstration of a Neural Network on a Low-Power Embedded Processor

Convolutional Neural Net 0:01:12

Convolutional Neural Net implementation in FPGA (Demo)

Deep Neural Network 0:25:08

Deep Neural Network Accelerators for IoT Applications

HC29-S9: Server 2:17:53

HC29-S9: Server

TensorDash: Exploiting Sparsity 0:12:43

TensorDash: Exploiting Sparsity to Accelerate Deep Neural Network Training

SysML 19: Paul 0:18:05

SysML 19: Paul Whatmough, FixyNN

HC30-S6: New Technologies 1:06:29

HC30-S6: New Technologies

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HotChips 2018 - DNN training, part1

ARTIFICIAL neural network 0:21:53

ARTIFICIAL neural network - WikiVidi Documentary

The Explosion in 0:21:03

The Explosion in Neural Network Chips - Prof. Trevor Mudge, University of Michigan