ICON: An IR Drop Compensation Method at OU Granularity with Low Overhead for eNVM-based Accelerators

Processing at operating unit (OU) granularity can alleviate the program variation effect and ADC conversion overhead in emerging non-volatile memory (eNVM) based accelerators. However, our experiments show that the IR drop effect can severely decrease computing accuracy when processing at OU granularity. Moreover, the IR drop effect on the entire array differs from the IR drop effect on OUs, meaning compensating at OU granularity is necessary. We also notice that the IR drop effect differs among OUs, and previous IR drop mitigation methods introduce more latency, area, and power overhead to adapt to these differences. Compensation modules from their methods calibrated for one OU do not apply to other OUs and need to be configured for each OU compensation using configuration modules. This paper proposes ICON, an IR drop compensation method at OU granularity with low overhead for eNVM-based accelerators. In order to decrease compensation latency, area, and power overhead, we perform several optimizations. First, the designed compensation circuit is simplified and does not compensate for the IR drop effect caused by parasitic resistances inside an OU. This simplification is based on our observation that the parasitic wire resistances inside an OU can be ignored using the OU size mentioned in previous works. Second, the compensation circuit is designed without the help of configuration circuits. We take the IR drop differences among OUs as input parameters of the compensation circuit so that it can apply to all OUs. Furthermore, the compensation circuit is pipelined into six stages to increase throughput. Experiments show that our compensation method can overcome the IR drop problem when processing in the eNVM-based crossbar array at OU granularity, with 1.3x similar to 13x lower latency, 1.5x similar to 33.1x lower area, and 1.4x similar to 8.4x lower power overhead compared with state-of-the-art methods.