Conventionally, DNN models are trained once in the cloud and deployed in edge
devices such as cars, robots, or unmanned aerial vehicles (UAVs) for real-time
inference. However, there are many cases that require the models to adapt to
new environments, domains, or new users. In order to realize such domain
adaption or personalization, the models on devices need to be continuously
trained on the device. In this work, we design EF-Train, an efficient DNN
training accelerator with a unified channel-level parallelism-based convolution
kernel that can achieve end-to-end training on resource-limited low-power
edge-level FPGAs. It is challenging to implement on-device training on
resource-limited FPGAs due to the low efficiency caused by different memory
access patterns among forward, backward propagation, and weight update.
Therefore, we developed a data reshaping approach with intra-tile continuous
memory allocation and weight reuse. An analytical model is established to
automatically schedule computation and memory resources to achieve high energy
efficiency on edge FPGAs. The experimental results show that our design
achieves 46.99 GFLOPS and 6.09GFLOPS/W in terms of throughput and energy
efficiency, respectively.

本研究设计的 EF-Train 是一种高效的 DNN 训练加速器，通过数据重塑，并建立自动计算和内存资源调度的分析模型，在低功耗边缘级 FPGA 上实现端到端的训练，最终达到了 46.99 GFLOPS 和 6.09GFLOPS/W 的吞吐量和能量效率。