RANA: Towards Efficient Neural Acceleration with Refresh-Optimized Embedded DRAM

The growing size of convolutional neural networks (CNNs) requires large amounts of on-chip storage. In many CNN accelerators, their limited on-chip memory capacity causes massive off-chip memory access and leads to very high system energy consumption. Embedded DRAM (eDRAM), with higher density than SRAM, can be used to improve on-chip buffer capacity and reduce off-chip access. However, eDRAM requires periodic refresh to maintain data retention, which costs much energy consumption. Refresh is unnecessary if the data's lifetime in eDRAM is shorter than the eDRAM's retention time. Based on this principle, we propose a Retention-Aware Neural Acceleration (RANA) framework for CNN accelerators to save total system energy consumption with refresh-optimized eDRAM. The RANA framework includes three levels of techniques: a retention-aware training method, a hybrid computation pattern and a refresh-optimized eDRAM controller. At the training level, CNN's error resilience is exploited in training to improve eDRAM's tolerable retention time. At the scheduling level, RANA assigns each CNN layer with a computation pattern that consumes the lowest energy. At the architecture level, a refresh-optimized eDRAM controller is proposed to alleviate unnecessary refresh operations. We implement an evaluation platform to verify RANA. Owing to the RANA framework, 99.7% eDRAM refresh operations can be removed with negligible performance and accuracy loss. Compared with the conventional SRAM-based CNN accelerator, an eDRAM-based CNN accelerator strengthened by RANA can save 41.7% off-chip memory access and 66.2% system energy consumption, with the same area cost.