Presentation
A Fast IR-drop Modeling for In-RRAM Computing Considering Data Allocation
DescriptionIn recent years, In-RRAM Computing (IRC) is a promising technique for deep neural network (DNN) applications. Combined with proper pruning technique, the cost and energy of DNN computation can be further reduced. However, IRC often suffers from various non-ideal effects in RRAM arrays such as the sneak path and IR-drop, which greatly affect the computation accuracy. Therefore, accurate error injection is required in the verification at early design stage. Conventional random disturbance and equation-based approaches did not consider the data allocation issue, which may incur larger errors for the sparse matrix generated by data pruning techniques. In this paper, a fast and accurate IR-drop model is proposed to reflect the data-dependent effects, which is able to offer accurate error injection in the DNN training phase with sparse matrix. As shown in the experimental results, the proposed model shows a good match to the HSPICE results even if the data allocation becomes non-uniform. With the proposed simple model, the accuracy degradation of real NN applications can be well observed, even for large RRAM arrays.
Event Type
Work-in-Progress Poster
TimeWednesday, June 265:00pm - 6:00pm PDT
LocationLevel 2 Lobby
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