Invention Grant
- Patent Title: Scaled compute fabric for accelerated deep learning
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Application No.: US17271801Application Date: 2019-08-11
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Publication No.: US11328207B2Publication Date: 2022-05-10
- Inventor: Gary R. Lauterbach , Sean Lie , Michael Morrison , Michael Edwin James , Srikanth Arekapudi
- Applicant: Cerebras Systems Inc.
- Applicant Address: US CA Sunnyvale
- Assignee: Cerebras Systems Inc.
- Current Assignee: Cerebras Systems Inc.
- Current Assignee Address: US CA Sunnyvale
- Agency: PatentVentures
- Agent Bennett Smith
- International Application: PCT/IB2019/056825 WO 20190811
- International Announcement: WO2020/044152 WO 20200305
- Main IPC: G06N3/04
- IPC: G06N3/04 ; G06N3/063

Abstract:
Techniques in advanced deep learning provide improvements in one or more of accuracy, performance, energy efficiency, and cost. In a first embodiment, a scaled array of processing elements is implementable with varying dimensions of the processing elements to enable varying price/performance systems. In a second embodiment, an array of clusters communicates via high-speed serial channels. The array and the channels are implemented on a Printed Circuit Board (PCB). Each cluster comprises respective processing and memory elements. Each cluster is implemented via a plurality of 3D-stacked dice, 2.5D-stacked dice, or both in a Ball Grid Array (BGA). A processing portion of the cluster is implemented via one or more Processing Element (PE) dice of the stacked dice. A memory portion of the cluster is implemented via one or more High Bandwidth Memory (HBM) dice of the stacked dice.
Public/Granted literature
- US20210248453A1 SCALED COMPUTE FABRIC FOR ACCELERATED DEEP LEARNING Public/Granted day:2021-08-12
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