| Autor: |
Büchel, Julian, Vasilopoulos, Athanasios, Kersting, Benedikt, Odermatt, Frederic, Brew, Kevin, Ok, Injo, Choi, Sam, Saraf, Iqbal, Chan, Victor, Philip, Timothy, Saulnier, Nicole, Narayanan, Vijay, Gallo, Manuel Le, Sebastian, Abu |
| Rok vydání: |
2023 |
| Předmět: |
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| Zdroj: |
2022 International Electron Devices Meeting (IEDM), San Francisco, CA, USA, 2022, pp. 33.1.1-33.1.4 |
| Druh dokumentu: |
Working Paper |
| DOI: |
10.1109/IEDM45625.2022.10019486 |
| Popis: |
The precise programming of crossbar arrays of unit-cells is crucial for obtaining high matrix-vector-multiplication (MVM) accuracy in analog in-memory computing (AIMC) cores. We propose a radically different approach based on directly minimizing the MVM error using gradient descent with synthetic random input data. Our method significantly reduces the MVM error compared with conventional unit-cell by unit-cell iterative programming. It also eliminates the need for high-resolution analog-to-digital converters (ADCs) to read the small unit-cell conductance during programming. Our method improves the experimental inference accuracy of ResNet-9 implemented on two phase-change memory (PCM)-based AIMC cores by 1.26%. |
| Databáze: |
arXiv |
| Externí odkaz: |
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