Abstract
This investigation proposes a computing-in-memory (CIM) design to circumvent the von Neumann bottleneck which causes limited computation throughput for effective artificial intelligence (AI) applications. The proposed CIM performs multiple operations such as single-instruction basic Boolean operations, addition, and signed number multiplication, and multiple functions such as normal mode and retention mode for the built-in self-test (BIST). Its 2T-Switch requires only two transistors to be utilized for static random-access memory (SRAM) array; thus, the arithmetic unit can be chosen easily and the area overhead is minimized. Its ripple carry adder and multiplier (RCAM) unit based on single-ended disturb-free 7T 1-Kb SRAM was developed using the full swing-gate diffusion input (FS-GDI) technology that has full voltage swing resolution, low power consumption, and less chip area cost. Its Auto-Switching Write Back Circuit restores addition and multiplication operations automatically to assigned memory address. The CIM is implemented using the TSMC 40-nm CMOS process, where the core area is $432.81 \times 510.265\,\,\mu \text{m}^{2}$. Among the related works, the proposed CIM performs the most number of operations and functions.
Original language | English |
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Pages (from-to) | 2172-2185 |
Number of pages | 14 |
Journal | IEEE Transactions on Very Large Scale Integration (VLSI) Systems |
Volume | 29 |
Issue number | 12 |
DOIs | |
Publication status | Published - 2021 Dec 1 |
Keywords
- Computing-in-memory (CIM)
- disturb-free
- full swing-gate diffusion input (FS-GDI)
- static random-access memory (SRAM)
- von Neumann bottleneck
ASJC Scopus subject areas
- Software
- Hardware and Architecture
- Electrical and Electronic Engineering