To efficiently implement searching and logic functions with the SRAM-based in-memory computing (IMC), we need to perform computations on bitlines (BLs) (called compute access) via multiple wordline (WL) activations. However, this may cause prominent read disturbance when the IMC is implemented with the standard 6 T SRAM. To address this reliability issue, existing solutions adopt either auxiliary assistance circuits or alternative bitcell topologies, but they lead to substantial overheads of the access speed or array density. In this article, we propose a novel 8T compute SRAM (CSRAM) for reliable and high-speed in-memory searching and compound logic-in-memory computations. Our 8T CSRAM features a pair of pMOS access transistors and split-WLs dedicated to the compute access. A thorough circuit-level analysis reveals that the pMOS-based compute access port is essential for significantly mitigating the read disturbance. Moreover, we propose an elevated precharge voltage scheme and a low-skewed inverter-based sensing amplifier to improve the sensing speed. We have validated the proposed 8T CSRAM design in a 16 Kb array with a 28-nm CMOS technology. Compared to the state-of-the-art 8 T CSRAM, results show that our design is not only reliable but also 3.1 times faster, with a maximum operating frequency upping to 2.44 GHz.

On-chip neural data compression is an enabling technique for wireless neural interfaces that suffer from insufficient bandwidth and power budgets to transmit the raw data. The data compression algorithm and its implementation should be power and area efficient and functionally reliable over different datasets. Compressed sensing is an emerging technique that has been applied to compress various neurophysiological data. However, the state-of-the-art compressed sensing (CS) encoders leverage random but dense binary measurement matrices, which incur substantial implementation costs on both power and area that could offset the benefits from the reduced wireless data rate. In this paper, we propose two CS encoder designs based on sparse measurement matrices that could lead to efficient hardware implementation. Specifically, two different approaches for the construction of sparse measurement matrices, i.e., the deterministic quasi-cyclic array code (QCAC) matrix and $(1,s)$-sparse random binary matrix [$(1,s)$-SRBM] are exploited. We demonstrate that the proposed CS encoders lead to comparable recovery performance. And efficient VLSI architecture designs are proposed for QCAC-CS and $(1,s)$-SRBM encoders with reduced area and total power consumption.

In-SRAM Computation improves the throughput and energy-efficiency of data-intensive applications by utilizing parallelism and reducing the data transfers. However, when multiple wordlines are accessed simultaneously, a short-circuit path will likely incur dynamic read disturbance and generate extra direct current in 6T Compute SRAM (CSRAM). In order to mitigate this issue, existing works either degrade the access speed, use area-hungry bitcells, or incur architecture-level overheads. In this paper, we first perform a comprehensive circuit-level analysis of the dynamic read disturbance issues of 6T SRAM for the first time and find that such disturbance can be efficiently avoided by maintaining the bitline voltage at a high level. Second, we propose a novel energy-efficient, reconfigurable sense amplifier design that is able to achieve fast and reliable sensing when the bitline voltage level is high for the compute access. Third, we propose an adaptive wordline control scheme that keeps the bitline voltage at a high level to eliminate the dynamic read disturbance and the sneaky direct current pathway. Both the new sense amplifier and adaptive wordline control are also optimized to support the normal read access efficiently. We have validated our design in a 55nm CMOS technology. Experimental results show that our design not only reliably addresses the read disturbance and the extra direct current, but also operates 19% faster than the state-of-the-art design using an advanced 28nm FDSOI technology.

Due to the increasing threats from possible large-scale quantum computers, post-quantum cryptography (PQC) has drawn significant attention from various communities recently. In particular, along with the National Institute of Standards and Technology (NIST) PQC standardization process, more works have gradually switched to the PQC hardware implementations. Following this trend, this tutorial brief, led by a group of experts in the field, aims to deliver a comprehensive tutorial on hardware circuits and systems design for PQC. After introducing primary arithmetic operations and algorithmic features of different PQC, we introduced related PQC hardware circuits and systems design techniques (from component to system levels). Future research and directions are also provided. This tutorial will provide useful information for the TCAS-II community and the broader Circuits and Systems Society.

Neural recording system miniaturization and integration with low-power wireless technologies require compressing neural data before transmission. Feature extraction is a procedure to represent data in a low-dimensional space; its integration into a recording chip can be an efficient approach to compress neural data. In this paper, we propose a streaming principal component analysis algorithm and its microchip implementation to compress multichannel local field potential (LFP) and spike data. The circuits have been designed in a 65-nm CMOS technology and occupy a silicon area of 0.06 mm ² . Throughout the experiments, the chip compresses LFPs by 10× at the expense of as low as 1% reconstruction errors and 144-nW/channel power consumption; for spikes, the achieved compression ratio is 25× with ~8% reconstruction errors and 3.05-μW/channel power consumption. In addition, the algorithm and its hardware architecture can swiftly adapt to nonstationary spiking activities, which enables efficient hardware sharing among multiple channels to support a high-channel count recorder.