公开(公告)号：US11270197B2

公开(公告)日：2022-03-08

申请号：US16672918

申请日：2019-11-04

Applicant: NVIDIA Corp.

Inventor： Yakun Shao ,  Rangharajan Venkatesan ,  Miaorong Wang ,  Daniel Smith ,  William James Dally ,  Joel Emer ,  Stephen W. Keckler ,  Brucek Khailany

IPC: G06F17/16 ,  G06N3/063 ,  G06F9/38 ,  G06N3/08

Abstract: A distributed deep neural net (DNN) utilizing a distributed, tile-based architecture includes multiple chips, each with a central processing element, a global memory buffer, and a plurality of additional processing elements. Each additional processing element includes a weight buffer, an activation buffer, and vector multiply-accumulate units to combine, in parallel, the weight values and the activation values using stationary data flows.

公开(公告)号：US20220076110A1

公开(公告)日：2022-03-10

申请号：US17530852

申请日：2021-11-19

Applicant: NVIDIA Corp.

Inventor： Yakun Shao ,  Rangharajan Venkatesan ,  Miaorong Wang ,  Daniel Smith ,  William James Dally ,  Joel Emer ,  Stephen W. Keckler ,  Brucek Khailany

IPC: G06N3/063 ,  G06F9/38 ,  G06F17/16 ,  G06N3/08

Abstract: A distributed deep neural net (DNN) utilizing a distributed, tile-based architecture includes multiple chips, each with a central processing element, a global memory buffer, and a plurality of additional processing elements. Each additional processing element includes a weight buffer, an activation buffer, and vector multiply-accumulate units to combine, in parallel, the weight values and the activation values using stationary data flows.

公开(公告)号：US20200293867A1

公开(公告)日：2020-09-17

申请号：US16672918

申请日：2019-11-04

Applicant: NVIDIA Corp.

Inventor： Yakun Shao ,  Rangharajan Venkatesan ,  Miaorong Wang ,  Daniel Smith ,  William James Dally ,  Joel Emer ,  Stephen W. Keckler ,  Brucek Khailany

IPC: G06N3/063 ,  G06N3/08 ,  G06F17/16 ,  G06F9/38

Abstract: A distributed deep neural net (DNN) utilizing a distributed, tile-based architecture includes multiple chips, each with a central processing element, a global memory buffer, and a plurality of additional processing elements. Each additional processing element includes a weight buffer, an activation buffer, and vector multiply-accumulate units to combine, in parallel, the weight values and the activation values using stationary data flows.