TinyML Talks - SRAM Based In-Memory Computing for Energy-Efficient AI Inference

TinyML Talks - SRAM Based In-Memory Computing for Energy-Efficient AI Inference

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Intro

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1 of 22

Intro

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TinyML Talks - SRAM Based In-Memory Computing for Energy-Efficient AI Inference

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  1. 1 Intro
  2. 2 ML collaboration with
  3. 3 Success of Deep Learning / AI
  4. 4 AI Algorithm & Edge Hardware
  5. 5 Typical DNN Accelerators
  6. 6 Eyeriss (JSSC 2017)
  7. 7 MCM Accelerator (JSSC 2020)
  8. 8 Bottleneck of All-Digital DNN HW Energy/Power
  9. 9 In-Memory Computing for DNNS
  10. 10 Analog IMC for SRAM Column
  11. 11 Analog SRAM IMC - Resistive
  12. 12 Analog SRAM IMC - Capacitive
  13. 13 ADC Optimization for IMC
  14. 14 Proposed IMC SRAM Macro Prototypes
  15. 15 Going Beyond IMC Macro Design
  16. 16 PIMCA: Programmable IMC Accelerator
  17. 17 IMC Modeling Framework
  18. 18 IMC HW Noise-Aware Training & Inference
  19. 19 Black-box Adversarial Input Attack
  20. 20 Pruning of Crossbar-based IMC Hardware
  21. 21 Acknowledgements
  22. 22 Contact Information

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