NicePIM: Design Space Exploration for Processing-In-Memory DNN
  Accelerators with 3D-Stacked-DRAM
Junpeng Wang Mengke Ge Bo Ding Qi Xu Song Chen Yi Kang
With the widespread use of deep neural networks(DNNs) in intelligent systems,
DNN accelerators with high performance and energy efficiency are greatly
demanded. As one of the feasible processing-in-memory(PIM) architectures,
3D-stacked-DRAM-based PIM(DRAM-PIM) architecture enables large-capacity memory
and low-cost memory access, which is a promising solution for DNN accelerators
with better performance and energy efficiency. However, the low-cost
characteristics of stacked DRAM and the distributed manner of memory access and
data storing require us to rebalance the hardware design and DNN mapping. In
this paper, we propose NicePIM to efficiently explore the design space of
hardware architecture and DNN mapping of DRAM-PIM accelerators, which consists
of three key components: PIM-Tuner, PIM-Mapper and Data-Scheduler. PIM-Tuner
optimizes the hardware configurations leveraging a DNN model for classifying
area-compliant architectures and a deep kernel learning model for identifying
better hardware parameters. PIM-Mapper explores a variety of DNN mapping
configurations, including parallelism between branches of DNN, DNN layer
partitioning, DRAM capacity allocation and data layout pattern in DRAM to
generate high-hardware-utilization DNN mapping schemes for various hardware
configurations. The Data-Scheduler employs an integer-linear-programming-based
data scheduling algorithm to alleviate the inter-PIM-node communication
overhead of data-sharing brought by DNN layer partitioning. Experimental
results demonstrate that NicePIM can optimize hardware configurations for
DRAM-PIM systems effectively and can generate high-quality DNN mapping schemes
with latency and energy cost reduced by 37% and 28% on average respectively
compared to the baseline method.