Yulan Liu

The von Neumann architecture is no longer sufficient for handling large-scale data. In-memory computing has emerged as the potent method for breaking through the memory bottleneck. A new 10T SRAM bitcell with row and column control lines called RC-SRAM is proposed in this article. The architecture based on RC-SRAM can achieve bi-directional and operand-controllable logic-in-memory and search operations through different signal configurations, which can comprehensively respond to various occasions and needs. Moreover, we propose threshold-controlled logic gates for sensing, which effectively reduces the circuit area and improves accuracy. We validate the RC-SRAM with a 28 nm CMOS technology, and the results show that the circuits are not only full featured and flexible for customization but also have a significant increase in the working frequency. At VDD = 0.9 V and T = 25 °C, the bi-directional search frequency is up to 775 MHz and 567 MHz, and the speeds for row and column Boolean logic reach 759 MHz and 683 MHz.

This article proposes a novel design for an in-memory computing SRAM, the DAM SRAM CORE, which integrates storage and computational functionality within a unified 11T SRAM cell and enables the performance of large-scale parallel Multiply–Accumulate (MAC) operations within the SRAM array. This design not only improves the area efficiency of the individual cells but also realizes a compact layout. A key highlight of this design is its employment of a dynamic aXNOR-based computation mode, which significantly reduces the consumption of both dynamic and static power during the computational process within the array. Additionally, the design innovatively incorporates a self-stabilizing voltage gradient quantization circuit, which enhances the computational accuracy of the overall system. The 64 × 64 bit DAM SRAM CORE in-memory computing core was fabricated using the 55 nm CMOS logic process and validated via simulations. The experimental results show that this core can deliver 5-bit output results with 1-bit input feature data and 1-bit weight data, while maintaining a static power consumption of 0.48 mW/mm² and a computational power consumption of 11.367 mW/mm². This showcases its excellent low-power characteristics. Furthermore, the core achieves a data throughput of 109.75 GOPS and exhibits an impressive energy efficiency of 21.95 TOPS/W, which robustly validate the effectiveness and advanced nature of the proposed in-memory computing core design.

Non-linear distortion of signals is a serious problem in computing-in-memory SRAM (CIM-SRAM) circuits in current mode. This problem greatly limits the performance of calculations and directly affects the computing power of the CIM-SRAM. In this study, the causes of non-linearity and inconsistency were investigated. Based on detailed analyses, we proposed a high-precision, fully dynamic range IV (HFIV) conversion circuit. The HFIV circuit was added to each bit line (BL) for voltage clamping and proportionally mirroring the read current. We applied the structure to numerous prior studies and evaluated them using the 55 nm complementary metal-oxide semiconductor process. The results showed the proposed HFIV circuit could increase the CIM-SRAM’s calculation linearity to 99.92% (8~32 SRAM bit-cells) and 99.8% (32~64 SRAM bit-cells) with a 1.2 V supply.