- Patent Title: Mixed signal neuromorphic computing with nonvolatile memory devices
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Application No.: US16608006Application Date: 2018-04-27
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Publication No.: US12106211B2Publication Date: 2024-10-01
- Inventor: Dmitri Strukov , Farnood Merrikh Bayat , Mohammad Bavandpour , Mohammad Reza Mahmoodi , Xinjie Guo
- Applicant: The Regents of the University of California
- Applicant Address: US CA Oakland
- Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee: THE REGENTS OF THE UNIVERSITY OF CALIFORNIA
- Current Assignee Address: US CA Oakland
- Agency: GATES & COOPER LLP
- International Application: PCT/US2018/029970 2018.04.27
- International Announcement: WO2018/201060A 2018.11.01
- Date entered country: 2019-10-24
- Main IPC: G06N3/049
- IPC: G06N3/049 ; G06N3/065 ; G11C16/04 ; H01L29/423

Abstract:
Building blocks for implementing Vector-by-Matrix Multiplication (VMM) are implemented with analog circuitry including non-volatile memory devices (flash transistors) and using in-memory computation. In one example, improved performance and more accurate VMM is achieved in arrays including multi-gate flash transistors when computation uses a control gate or the combination of control gate and word line (instead of using the word line alone). In another example, very fast weight programming of the arrays is achieved using a novel programming protocol. In yet another example, higher density and faster array programming is achieved when the gate(s) responsible for erasing devices, or the source line, are re-routed across different rows, e.g., in a zigzag form. In yet another embodiment a neural network is provided with nonlinear synaptic weights implemented with nonvolatile memory devices.
Public/Granted literature
- US20210019609A1 MIXED SIGNAL NEUROMORPHIC COMPUTING WITH NONVOLATILE MEMORY DEVICES Public/Granted day:2021-01-21
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