Two-Bit Networks for Deep Learning on Resource-Constrained Embedded Devices
This addresses the need for efficient deep learning on IoT and edge devices with limited resources, representing an incremental improvement in model compression techniques.
The paper tackles the problem of deploying deep learning on resource-constrained embedded devices by proposing Two-Bit Networks (TBNs) that compress CNNs with edge weights limited to (-2, -1, 1, 2), encoded in two bits, significantly reducing memory usage and improving computational efficiency while maintaining good classification accuracy.
With the rapid proliferation of Internet of Things and intelligent edge devices, there is an increasing need for implementing machine learning algorithms, including deep learning, on resource-constrained mobile embedded devices with limited memory and computation power. Typical large Convolutional Neural Networks (CNNs) need large amounts of memory and computational power, and cannot be deployed on embedded devices efficiently. We present Two-Bit Networks (TBNs) for model compression of CNNs with edge weights constrained to (-2, -1, 1, 2), which can be encoded with two bits. Our approach can reduce the memory usage and improve computational efficiency significantly while achieving good performance in terms of classification accuracy, thus representing a reasonable tradeoff between model size and performance.