Aug 2026· Midwest Symposium on Circuits and Systems· pp. 435-439· 0 citations· 17 references
Abstract
This paper proposes a novel compute-in-memory architecture comprising multi-valued memory units using binary nonvolatile memory devices (NVMs) for brainmorphic circuits. The proposed architecture employs parallel-connected binary nonvolatile memory devices with thermometer-code representation to mitigate the impact of write errors while enabling addition and subtraction of memory values. The feedback write scheme conducts addition and subtraction of memory values on-chip across an array of multi-valued memory units. In this paper, we consider the implementation of voltage-controlled MRAM (VC-MRAM), which offers high endurance and low-voltage operation, making it suitable for brainmorphic hardware. To verify the circuit operation, we fabricated a CMOS-integrated test chip. This test chip consists of a single memory unit and a feedback write circuit, where the VC-MRAM devices were replaced with NMOS pseudo-resistive transistors to emulate NVM resistance states. Measurements showed less than 5% error compared to SPICE simulations, confirming accurate memoryto-PWM conversion. Numerical analysis further demonstrated an $\mathbf{8 7 \% - 9 5 \%}$ discrimination probability under 5% resistance variation. These results show the suitability of the architecture for multi-valued weight updates in robust brainmorphic systems.
Conventional computing architectures are reaching their scalability limits, while their energy demands increase rapidly. A bottleneck is the separation of memory and processing units, which requires a continuous data transfer. This memory wall increases the power consumption and limits the processing speed at the same...
L. Brackmann, Tobias Ziegler, N. Kopperberg et al.· npj Unconventional Computing· 0 citations
With the rapid advancement of artificial intelligence (AI), there is an urgent demand for emerging technologies that can deliver low-power storage alongside high-performance computing. Traditional volatile memories—such as static random access memory and dynamic random access memory—which are closely integrated with th...
Daphne Chen, M. Kozicki, Tuo-Hung Hou· Nanotechnology· 0 citations
We report a 4-transistor (4T) Ternary Content Addressable Memory (TCAM) that significantly reduces the physical footprint by employing dual-gate IGZO transistors. High-density TCAMs are essential components for memory augmented neural networks and advanced computing systems; however, the large physical footprint remain...
Taewon Seo, Taejun Ha, Seunghyun Son et al.· IEEE Electron Device Letters· 0 citations
This work presents a schematic-level digital near-memory computing architecture based on a 1-Transistor-3-Resistor (1T3R) bit-slicing scheme for 3-bit signed weight storage and indicates that the proposed architecture can maintain functional classification capability under 3-bit weight and 2-bit input constraints.
Zeyuan Hou, Xiao-Meng Wang, Yang Yi· Journal of Electronics and E...· 0 citations
This paper proposes a computing-in-memory (CiM) architecture based on a hybrid 2T-2C/4T-2C ferroelectric random-access memory (FRAM) array that provides ADC-free bitwise computing with binary inputs and outputs, reducing peripheral-circuit overhead and power consumption.
Chen He, Jianjun Li, Wei Li et al.· Electronics· 0 citations
The energy cost of data movement between static random-access memory (SRAM) and arithmetic units has become a critical limitation in edge artificial-intelligence accelerators. SRAM-based compute-in-memory (CIM) alleviates this bottleneck by executing multiply-and-accumulate operations near or inside the memory array. T...
Bitla Prabhakar T. Satyanarayana, Dr. Malothu Amru, Dr. Somala Rama Kishore· International Journal of Adv...· 0 citations
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