GhostShiftAddNet: More Features from Energy-Efficient Operations
- URL: http://arxiv.org/abs/2109.09495v1
- Date: Mon, 20 Sep 2021 12:50:42 GMT
- Title: GhostShiftAddNet: More Features from Energy-Efficient Operations
- Authors: Jia Bi, Jonathon Hare, Geoff V. Merrett
- Abstract summary: Deep convolutional neural networks (CNNs) are computationally and memory intensive.
This paper proposes GhostShiftAddNet, where the motivation is to implement a hardware-efficient deep network.
We introduce a new bottleneck block, GhostSA, that converts all multiplications in the block to cheap operations.
- Score: 1.2891210250935146
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep convolutional neural networks (CNNs) are computationally and memory
intensive. In CNNs, intensive multiplication can have resource implications
that may challenge the ability for effective deployment of inference on
resource-constrained edge devices. This paper proposes GhostShiftAddNet, where
the motivation is to implement a hardware-efficient deep network: a
multiplication-free CNN with fewer redundant features. We introduce a new
bottleneck block, GhostSA, that converts all multiplications in the block to
cheap operations. The bottleneck uses an appropriate number of bit-shift
filters to process intrinsic feature maps, then applies a series of
transformations that consist of bit-wise shifts with addition operations to
generate more feature maps that fully learn to capture information underlying
intrinsic features. We schedule the number of bit-shift and addition operations
for different hardware platforms. We conduct extensive experiments and ablation
studies with desktop and embedded (Jetson Nano) devices for implementation and
measurements. We demonstrate the proposed GhostSA block can replace bottleneck
blocks in the backbone of state-of-the-art networks architectures and gives
improved performance on image classification benchmarks. Further, our
GhostShiftAddNet can achieve higher classification accuracy with fewer FLOPs
and parameters (reduced by up to 3x) than GhostNet. When compared to GhostNet,
inference latency on the Jetson Nano is improved by 1.3x and 2x on the GPU and
CPU respectively.
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