Abstract
Recently, eDRAM-based computing-in-memory operating in analog domain (ACIM) has been proposed to enhance area and energy efficiency of AI computations [1-5]. However, conventional ACIMs have three challenges. First, previous structures [1-3] have primarily focused on computing area or energy efficiency without considering refresh overhead. This may not be a problem in small DNN models. However, as shown in Fig. 1, refresh overhead has become critical with the increase in DNN model size for higher accuracy and the decrease in data retention time (DRT) due to technology scaling. Second, a pre-measured refresh period at the worst corner used in [4, 5] causes unnecessary energy consumption in most PVT corners, as DRT in eDRAM significantly varies depending on the PVT corner. The last challenge is the significant area and energy overhead of data conversion circuits (DAC or ADC) required in ACIMs to ensure DNN accuracy [4-8].
| Original language | English |
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| Title of host publication | 2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| ISBN (Electronic) | 9798350376326 |
| DOIs | |
| Publication status | Published - 2024 |
| Event | 2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024 - Hiroshima, Japan Duration: 2024 Nov 18 → 2024 Nov 21 |
Publication series
| Name | 2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024 |
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Conference
| Conference | 2024 IEEE Asian Solid-State Circuits Conference, A-SSCC 2024 |
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| Country/Territory | Japan |
| City | Hiroshima |
| Period | 24/11/18 → 24/11/21 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
All Science Journal Classification (ASJC) codes
- Hardware and Architecture
- Energy Engineering and Power Technology
- Electrical and Electronic Engineering
- Control and Optimization