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Compute-in-memory (CIM) is primarily built into neuromorphic hardware to radically subvert the modern computing bottleneck for a range of applications, in particular for today's artificial intelligence (AI) related workloads. Emerging computational memory technologies, such as resistive random-access memory (RRAM), offer clear advantages in CIM to perform tasks in place in the memory itself, providing significant improvements in latency and energy efficiency. In this article, we showcase an innovative memristive circuit in a 6-transistor-1-RRAM (6T1R) configuration to enable a faster yet more efficient CIM for AI applications. In practice, our 6T1R cell leverages a series of pulse-width-modulated (PWM) pulses as computing variables for energy efficiency. In a way to support AI models for simplicity and robustness, individual 6T1R cell can be programmed to encode either positive or negative weight values by measuring the direction of current through the RRAM in addition to the multi-bit computation capability. For computational accuracy, faulty RRAM can be quarantined from the network regardless of its resistance values by setting the particular cell into the high-impedance state. A proof-of-concept validation was conducted using a custom 65 nm CMOS/RRAM technology node. The parallel inter-tile communication would achieve up to 1.6 trillion operations per second (TPOS) in data throughout with a computational efficiency reached to 1.07 TOPS/W.
Bai et al. (Tue,) studied this question.