  Data processing on convolutional neural networks (CNNs) places a heavy burden
on energy-constrained mobile platforms. This work optimizes energy on a mobile
client by partitioning CNN computations between in situ processing on the
client and offloaded computations in the cloud. A new analytical CNN energy
model is formulated, capturing all major components of the in situ computation,
for ASIC-based deep learning accelerators. The model is benchmarked against
measured silicon data. The analytical framework is used to determine the
optimal energy partition point between the client and the cloud at runtime. On
standard CNN topologies, partitioned computation is demonstrated to provide
significant energy savings on the client over fully cloud-based or fully in
situ computation. For example, at 80 Mbps effective bit rate and 0.78 W
transmission power, the optimal partition for AlexNet [SqueezeNet] saves up to
52.4% [73.4%] energy over a fully cloud-based computation, and 27.3% [28.8%]
energy over a fully in situ computation.
