We present a novel dynamic configuration technique for deep neural networks
that permits step-wise energy-accuracy trade-offs during runtime. Our
configuration technique adjusts the number of channels in the network
dynamically depending on response time, power, and accuracy targets. To enable
this dynamic configuration technique, we co-design a new training algorithm,
where the network is incrementally trained such that the weights in channels
trained in earlier steps are fixed. Our technique provides the flexibility of
multiple networks while storing and utilizing one set of weights. We evaluate
our techniques using both an ASIC-based hardware accelerator as well as a
low-power embedded GPGPU and show that our approach leads to only a small or
negligible loss in the final network accuracy. We analyze the performance of
our proposed methodology using three well-known networks for MNIST, CIFAR-10,
and SVHN datasets, and we show that we are able to achieve up to 95% energy
reduction with less than 1% accuracy loss across the three benchmarks. In
addition, compared to prior work on dynamic network reconfiguration, we show
that our approach leads to approximately 50% savings in storage requirements,
while achieving similar accuracy.

提出一种新的动态配置技术，可在运行时进行逐步的能量 - 精确度权衡，调整深度神经网络中通道数量，以响应时间、功率和准确性目标调整，并且我们对 MNIST、CIFAR-10 和 SVHN 数据集的三个知名网络进行了性能分析并证明，我们能够在三个基准测试中实现高达 95% 的能量降低且不到 1% 的准确率损失。