To deploy deep neural networks on resource-limited devices, quantization has been widely explored. In this work, we study the extremely low-bit networks which have tremendous speed-up, memory saving with quantized activation and weights. We first bring up three omitted issues in extremely low-bit networks: the squashing range of quantized values; the gradient vanishing during backpropagation and the unexploited hardware acceleration of ternary networks. By reparameterizing quantized activation and weights vector with full precision scale and offset for fixed ternary vector, we decouple the range and magnitude from direction to extenuate above problems. Learnable scale and offset can automatically adjust the range of quantized values and sparsity without gradient vanishing. A novel encoding and computation pattern are designed to support efficient computing for our reparameterized ternary network (RTN). Experiments on ResNet-18 for ImageNet demonstrate that the proposed RTN finds a much better efficiency between bitwidth and accuracy and achieves up to 26.76% relative accuracy improvement compared with state-of-the-art methods. Moreover, we validate the proposed computation pattern on Field Programmable Gate Arrays (FPGA), and it brings 46.46 × and 89.17 × savings on power and area compared with the full precision convolution.

Urban driving requires the autonomous vehicles to drive with smooth control and track the planned path accurately. However, most of the existing path-tracking controllers pay more attention to the tracking errors than the smoothness because of the difficulties to balance them. This paper proposes a learning-based method to achieve the trade-off between the smooth control and the tracking-error control. An Reinforcement Learning algorithm, which is called Proximal Policy Optimization, is used to train a neural model to tune the weights of a designed controller PP_PID (Pure-Pursuit_Proportional Integral Derivative). The successfully trained model will adaptively select the optimal weights for the Pure-Pursuit and PID to guarantee the control smoothness and accuracy. Finally, the proposed controller will be tested in two path tracking scenarios. The results show that the proposed controller can change the weights adaptively to maintain a balance in the tracking error and lateral acceleration under the 35km/h.