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Computing-in-memory (CIM) based on embedded nonvolatile memory is a promising candidate for energy-efficient multiply-and-accumulate (MAC) operations in artificial intelligence (AI) edge devices. However, circuit design for NVM-based CIM (nvCIM) imposes a number of challenges, including an area-latency-energy tradeoff for multibit MAC operations, pattern-dependent degradation in signal margin, and small read margin. To overcome these challenges, this article proposes the following: 1) a serial-input non-weighted product (SINWP) structure; 2) a down-scaling weighted current translator (DSWCT) and positive–negative current-subtractor (PN-ISUB); 3) a current-aware bitline clamper (CABLC) scheme; and 4) a triple-margin small-offset current-mode sense amplifier (TMCSA). A 55-nm 1-Mb ReRAM-CIM macro was fabricated to demonstrate the MAC operation of 2-b-input, 3-b-weight with 4-b-out. This nvCIM macro achieved $T_{\text {MAC}}= 14.6$ ns at 4-b-out with peak energy efficiency of 53.17 TOPS/W.

Nonvolatile computing-in-memory (nvCIM) can improve the latency (t AC ) and energy-efficiency (EF MAC ) 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 (I BL ) resulting in an inaccurate BL-clamping voltage (V BLC ); as well as a large (I BL ) 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 (t AC ) in multiple cycles of binary WL inputs on 1T1R cells for multibit inputs. (3) Previous positive-negative-split weight-mapping consumes high total (l BL ) and area overhead (needing 2x(m-1) cells for a signed m-bit weight) for cell arrays with high-weight precision. (4) Long (t AC ) 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 (l BL ) for multibit inputs, increase IN#, and reduce the (l BL ) range/size for accurate (V BLC ) 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 l BL in the cell array. (3) A dual-bit small-offset current-mode sense amplifier (DbSO-CSA) to reduce IREF# and t AC . A fabricated 22nm 2Mb ReRAM-CIM macro presents the first 4b-input nvCIM macro, featuring a 9.8-18.3ns t AC and an EF MAC of 121.3-28.9TOPS/W from binary to 4bIN-4bW-11bOUT compute precisions.

Embedded nonvolatile memory (NVM) and computing-in-memory (CIM) are significantly reducing the latency (t MAC ) and energy consumption (E MAC ) of multiply- and-accumulate (MAC) operations in artificial intelligence (AI) edge devices [1, 2]. Previous ReRAM CIM macros demonstrated MAC operations for lb-input, ternary- weighted, 3b-output CNNs [1] or lb-input, 8b-weighted, 1b-output fully-connected networks with limited accuracy [2]. To support higher-accuracy convolution neural network heavy applications NVM-CIM should support multibit inputs/weights and multi-bit output (MAC-OUT) for CNN operations. One way to achieve multibit weights is to use a multi-level ReRAM cell to store the weight. However, as shown in Fig. 24.1.1, multibit ReRAM CIM faces several challenges. (1) a tradeoff between area and speed for multibit input/weight/MAC-OUT MAC operations; (2) sense amplifier’s high input offset, large area, and high parasitic load on the read-path due to large BL currents (I BL ) from multibit MAC; (3) limited accuracy due to a small read/sensing margin (I SM ) across MAC-OUT or variation in cell resistance (particularly MLC cells). To overcome these challenges, this work proposes, (1) a serial-input non-weighted product (SINWP) structure to optimize the tradeoff between area, t MAC and E MAC , (2) a down-scaling weighted current translator (DSWCT) and positive-negative current- subtractor (PN-ISUB) for short delay, a small offset and a compact read-path area; and (3) a triple-margin small-offset current-mode sense amplifier (TMCSA) to tolerate a small I SM . A fabricated 55nm 1Mb ReRAM-CIM macro is the first ReRAM CIM macro to support CNN operations using multibit input/weight MAC-OUT. This device achieves the shortest CIM-MAC-access time (t AC ) among existing ReRAM-CIMs (t MAC =14.6ns with 2b-input, 3b-weight with 4b-MAC-OUT) and the best peak E MAC of 53.17 TOPS/W (in binary mode).

Recent advances in neuromorphic computing system have shown resistive random-access memory (ReRAM) can be used to efficiently implement compact parallel computing arrays, which are inherently suitable for neural networks that require large amounts of matrix-vector multiplications (MVMs). In this work, we proposed a neuromorphic computing system based on ReRAM synaptic array to implement bitwise neural networks. The system contains a ReRAM synaptic array for parallel computation of bitwise MVMs, and a field-programmable gate array for data buffering and processing. To deploy the network on the system, a customized training scheme was required to adapt the trained network to the characteristic of ReRAM synaptic array with bitwise weights and inputs. We also managed the resolution of partial sum to reduce the bit width requirement of sense amplifier, thereby reducing power consumption. The measurement results show that the ReRAM synaptic array consumed only 0.27mW at 1V supply by using 1-bit sense amplifier while the system still maintained 97.52% accuracy on MNIST dataset.