Enabling Energy-Efficient Unsupervised Monocular Depth Estimation on ARMv7-Based Platforms
Valentino Peluso1, Antonio Cipolletta1, Andrea Calimera1, Matteo Poggi2, Fabio Tosi2 and Stefano Mattoccia2
1Politecnico di Torino, Italy
2Università di Bologna, Italy
ABSTRACT
This work deals with the implementation of energyefficient monocular depth estimation using a low-cost CPU for low-power embedded systems. It first describes the PyD-Net depth estimation network, which consists of a lightweight CNN able to approach state-of-the-art accuracy with ultra-low resource usage. Then it proposes an accuracy-driven complexity reduction strategy based on a hardware-friendly fixed-point quantization. Finally, it introduces the low-level optimization enabling effective use of integer neural kernels. The objective is threefold: (i) prove the efficiency of the new quantization flow on a depth estimation network, that is, the capability to retaining the accuracy reached by floating-point arithmetic using 16- and 8-bit integers, (ii) demonstrate the portability of the quantized model into a generalpurpose 32-bit RISC architecture of the ARM Cortex family, (iii) quantify the accuracy-energy tradeoff of unsupervised monocular estimation to establish its use in the embedded domain. The experiments have been run on a Raspberry PI board powered by a Broadcom BCM2837 chipset. A parametric analysis conducted over the KITTI date-set shows marginal accuracy loss with 16-bit (8-bit) integers and energy savings up to 6.55× (9.23×) w.r.t. floating-point. Compared to high-end CPU and GPU the proposed solution improves scalability.
