Sparrow ECC: A Lightweight ECC Approach for HBM Refresh Reduction towards Energy-efficient DNN Inference

Exponential growth in deep neural network (DNN) model size has resulted in significant demands for memory bandwidth, leading to the extensive adoption of high bandwidth memory (HBM) in DNN inference. However, with the shorter retention time due to high operating temperature, HBM requires more frequent refresh operations, suffering larger refresh energy/performance overhead. In this paper, we propose Sparrow ECC, a lightweight but stronger HBM ECC technique for less refresh operations while preserving inference accuracy. Sparrow ECC exploits the dominant exponent pattern (i.e., value similarity) in pre-trained DNN weights, limiting the exponent value range of the pre-trained weights to prevent anomalously large weight value change due to the errors. In addition, through duplication and single error correction (SEC) code, Sparrow ECC strongly protects the critical bits in DNN weights. In our evaluation, when the proportion of 1→0 bit errors is 100% and 99%, Sparrow ECC reduces the refresh energy consumption by 90.40% and 93.22%, on average, respectively, compared to the state-of-the-art (RS(19,17)+ZEM [22]) refresh reduction technique, while preserving inference accuracy.