A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices

243Citations
Citations of this article
59Readers
Mendeley users who have this article in their library.
Get full text

Abstract

Nonvolatile computing-in-memory (nvCIM) can improve the latency (tAC) and energy-efficiency (EFMAC) of tiny AI edge devices performing multiply-and-accumulate (MAC) computing after system wake-up. Prior nvCIMs have proven effective for binary input (IN) and weight (W), and 3b output (OUT) [1], 1-8-1b IN-W-OUT [2], and 2-3-4b IN-W-OUT [3] neural networks; however, the higher precision (4-4b IN-W) for MAC operations is needed for multi-bit CNNs to achieved high-inference accuracy [4]. As Fig.15.4.1 shows, improving the precision of nvCIM macros involves various challenges. (1) A large number of activated WLs provides a wide range of BL current (IBL) resulting in an inaccurate BL-clamping voltage (VBLC); as well as a large (IBL) requiring a large array area due to the need for wide metal lines to support high-current density. (2) Previous 'WL = input' approaches suffer from: (a) few parallel inputs (IN#) due to (1), and (b) long (tAC) in multiple cycles of binary WL inputs on 1T1R cells for multibit inputs. (3) Previous positive-negative-split weight-mapping consumes high total (lBL) and area overhead (needing 2x(m-1) cells for a signed m-bit weight) for cell arrays with high-weight precision. (4) Long (tAC) and a large number of reference currents (IREF#) for high-precision outputs. To overcome these challenges, this work proposes: (1) a BL-IN-OUT multibit computing (BLIOMC) scheme using a single WL-on and input-aware multibit BL clamping (IA-MBC) to shorten (lBL) for multibit inputs, increase IN#, and reduce the (lBL) range/size for accurate (VBLC) and a compact array area. (2) Scrambled 2's complement (S2C) weight mapping (S2CWM), input-aware source-line (SL) voltage biasing (IA-SLVB), and an S2C value combiner (S2CVC) to reduce area overhead and lBL in the cell array. (3) A dual-bit small-offset current-mode sense amplifier (DbSO-CSA) to reduce IREF# and tAC. A fabricated 22nm 2Mb ReRAM-CIM macro presents the first 4b-input nvCIM macro, featuring a 9.8-18.3ns tAC and an EFMAC of 121.3-28.9TOPS/W from binary to 4bIN-4bW-11bOUT compute precisions.

Cite

CITATION STYLE

APA

Xue, C. X., Huang, T. Y., Liu, J. S., Chang, T. W., Kao, H. Y., Wang, J. H., … Chang, M. F. (2020). A 22nm 2Mb ReRAM Compute-in-Memory Macro with 121-28TOPS/W for Multibit MAC Computing for Tiny AI Edge Devices. In Digest of Technical Papers - IEEE International Solid-State Circuits Conference (Vol. 2020-February, pp. 244–246). Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ISSCC19947.2020.9063078

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free