Abstract-A hardware-efficient sigmoid function calculator with adjustable precision for neural network and deep learning applications is proposed in this paper. By adopting the bit-plane format of the input and output value, the computational latency of the processing time can be dynamically reduced according to the user configuration. To reduce the hardware cost, the coefficients used to calculate the sigmoid value can be shared for multiple calculators without any structural hazard. And the restricted constraint is applied in the coefficients training stage to further simplify the computation in the calculation stage with negligible quality loss. A test module is designed for the proposal and operated at 300MHz to achieve 75 million sigmoid calculations per second. Implemented in 90nm CMOS technology, the core of the calculator costs 1.6k gates, and a 1k bits globally shared memory is used to store the coefficients.
This paper proposes a group of macroblock (GOMB) based motion estimation (ME) algorithm supporting adaptive search range (ASR) for H.264 video coding. Adopting the concept of GOMB to generate the Predicted Motion Vector (PMV) for doing H.264 ME achieves good results in balancing the computational complexity, video quality and memory bandwidth. Moreover, it supports adaptive search range (ASR) in doing ME to greatly reduce both the computational complexity and memory bandwidth while maintaining good video quality. In HD1080 resolution, compared to the JM full search block matching algorithm (FSBMA) centered at (0, 0) and JM PMV-based block matching algorithm (BMA), the proposed algorithm respectively reduces about 93% and 81% memory bandwidth, as well as 99% and 76% computational complexity with negligible PSNR drop.
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