Jingtao Fan

Light has been intensively investigated as a computing medium due to its high-speed propagation and large operation bandwidth. Since the invention of the first laser in 1960, the development of optical computing technologies has presented both challenges and opportunities. Recent advances in artificial intelligence over the past decade have opened up new horizons for optical computing applications. This study presents an end-to-end truth table direct mapping approach using on-chip deep diffractive neural network (D²NN) technology to achieve highly parallel optical logic operations. To enable precise logical operations, we propose an on-chip nonlinear solution leveraging the similarity between the hyperbolic tangent (tanh) function and reverse saturable absorption characteristics of quantum dots. We design and demonstrate a 4-bit on-chip D²NN full adder circuit. The simulation results show that the proposed architecture achieves 100% accuracy for 4-bit full adders across the entire dataset.

Compressive sensing technique allows capturing fast phenomena at a much higher frame rate than the camera sensor, by recovering a frame sequence from their encoded combination. However, most conventional compressive video sensing methods limit the achieved frame rate improvement to tenfold and only support low resolution recovery. Making use of the camera's redundant spatial resolution for further frame rate improve, here we report a novel compressive video acquisition technique termed Sinusoidal Sampling Enhanced Compressive Camera (S2EC2) to encode denser frames within a snapshot. Specifically, we decompose the dense frames into groups and apply combinational coding: random codes within each group for compressive acquisition; group specific sinusoidal codes to multiplex different groups onto the high resolution sensor. The sinusoidal codes designed for these groups would shift their frequency components by different offsets in the Fourier domain and staggered the dominant frequencies of the coded measurements of these groups. Correspondingly, the reconstruction successfully separate coded measurements of different groups and recovers frames within each group. Besides, we also solve the implementation problem of insufficient gray scale spatial light modulation speed, and build a prototype achieving 2000 fps reconstruction with a 15.6 fps camera (the actual compression ratio is 0.009). The extensive experiments validate the proposed approach.

Cloud gaming (CG) system could pursue both high-quality gaming experience via intensive computing, and ultimate convenience anywhere at anytime through any energy-constrained mobile devices. Despite the abundance of efforts devoted, state-of-the-art CG systems still suffer from multiple key limitations: expensive deployment cost, high bandwidth consumption and unsatisfied quality of experience (QoE). As a result, existing works are not widely adopted in reality. This paper proposes a Transparent Gaming framework called T-Gaming that allows users to play any popular high-end desktop/console games on-the-fly over the Internet. T-Gaming utilizes the off-the-shelf consumer GPUs without resorting to the expensive proprietary GPU virtualization (vGPU) technology to reduce the deployment cost. Moreover, it enables prioritized video encoding based on the human visual feature to reduce the bandwidth consumption without noticeable visual quality degradation. Last but not least, T-Gaming adopts adaptive real-time streaming based on deep reinforcement learning (RL) to improve user's QoE. To evaluate the performance of T-Gaming, we implement and test a prototype system in the real world. Compared with the existing cloud gaming systems, T-Gaming not only reduces the expense per user by 75% hardware cost reduction and 14.3% network cost reduction, but also improves the normalized average QoE by 3.6%-27.9%.