Abstract
Computing-in-memory (CIM) architectures have paved the way for energy-efficient artificial intelligence (AI) systems while outperforming von Neumann architectures. In particular, resistive RAM (RRAM)-based CIM has drawn attention due to high cell density, non-volatility, and compatibility with a CMOS process. RRAM also exhibits the feasibility of high-capacity CIM with multi-bit encoding per cell exploiting an appropriate ON/OFF resistance ratio. However, the prior work regarding multi-level RRAM cells mainly focused on achieving higher bit resolution in write without consideration of CIM performance. Thus, the circuit solution to achieve multi-bit encoding per cell dedicated to RRAM-based CIM (RCIM) is of importance to support high-capacity AI systems with reliable CIM performance. This article presents a 256 times 256 CIM multi-level RRAM macro featuring iterative write with verification to achieve reliable multi-bit encoding per cell and the voltage-sensing readout circuit to surmount the underlying logic ambiguity in RCIM architectures. In addition, we also demonstrate the key design space of a fabricated RRAM array in the write operation with extensive experiments. The test chip fabricated in a Taiwan Semiconductor Manufacturing Company (TSMC) 40-nm CMOS and RRAM process achieves a peak energy efficiency of 118.44 TOPS/W in the ternary-weight multiply-and-accumulate (MAC) operation and demonstrates the feasibility of multi-level RCIM with voltage-sensing RCIM.
Original language | English |
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Pages (from-to) | 845-857 |
Number of pages | 13 |
Journal | IEEE Journal of Solid-State Circuits |
Volume | 57 |
Issue number | 3 |
DOIs | |
State | Published - 1 Mar 2022 |
Bibliographical note
Publisher Copyright:© 1966-2012 IEEE.
Keywords
- Computing-in-memory (CIM)
- convolutional neural network
- multi-level cell
- multiply-and-accumulate (MAC)
- processing-in-memory
- resistive RAM (RRAM)
- write verification