QuiltNet: Efficient Deep Learning Inference on Multi-Chip Accelerators Using Model Partitioning

Abstract

We have seen many successful deployments of deep learning accelerator designs on different platforms and technologies, e.g., FPGA, ASIC, and Processing In-Memory platforms. However, the size of the deep learning models keeps increasing, making computations a burden on the accelerators. A naive approach to resolve this issue is to design larger accelerators; however, it is not scalable due to high resource requirements, e.g., power consumption and off-chip memory sizes. A promising solution is to utilize multiple accelerators and use them as needed, similar to conventional multiprocessing. For example, for smaller networks, we may use a single accelerator, while we may use multiple accelerators with proper network partitioning for larger networks. However, partitioning DNN models into multiple parts leads to large communication overheads due to inter-layer communications. In this paper, we propose a scalable solution to accelerate DNN models on multiple devices by devising a new model partitioning technique. Our technique transforms a DNN model into layer-wise partitioned models using an autoencoder. Since the autoencoder encodes a tensor output into a smaller dimension, we can split the neural network model into multiple pieces while significantly reducing the communication overhead to pipeline them. Our evaluation results conducted on state-of-the-art deep learning models show that the proposed technique significantly improves performance and energy efficiency. Our solution increases performance and energy efficiency by up to 30.5% and 28.4% with minimal accuracy loss as compared to running the same model on pipelined multi-block accelerators without the autoencoder.