Natural motion mapping enhances the gaming experience by reducing the cognitive burden and increasing immersion. However, many players still use the keyboard and mouse in recent commercial PC games. To solve the conflict between complex avatar motion and the limited interaction system, we introduced CamTroller, an auxiliary tool for commercial one-to-one avatar mapping PC games following the concept of a NUI (natural user interface). To validate this concept, we selected PUBG as the application scenario and developed a proof-of-concept system to help players achieve a better experience by naturally mapping selected human motions to the avatars in games through an RGB webcam. A within-subject study with 18 non-professional players practiced common operation (Basic), professional player’s operation (Pro), and CamTroller. Results showed that the performance of CamTroller was as good as the Pro and significantly higher than Basic. Also, the subjective evaluation showed that CamTroller achieved significantly higher intuitiveness than Basic and Pro.

The state-of-the-art convolutional neural network accelerators are showing a growing interest in exploiting the bit-level sparsity and eliminating the ineffectual computations of zero bits. However, the excessive redundancy and the irregular distribution of nonzero bits limit the real speedup in the accelerators. To address this, we propose an algorithm-architecture codesign, named structured term pruning (STP), to boost the computation efficiency of neural networks inference. Specifically, we enhance the bit sparsity by guiding the weights toward the value with fewer power-of-two terms. Then, we structure the terms with layer-wise group budgets. Retraining is adopted to recover the accuracy drop. We also design the hardware of the group processing element and the fast signed-digital encoder for efficient implementation of STP networks. The system design of STP is realized with some easy alterations on an input stationary systolic array design. Extensive evaluation results demonstrate that STP can reduce significant inference computation costs, and achieve$2.35\times $computational energy saving for the ResNet18 network on the ImageNet dataset.

Deep Neural Networks (DNNs) have achieved remarkable success in various Artificial Intelligence applications. Quantization is a critical step in DNNs compression and acceleration for deployment. To further boost DNN execution efficiency, many works explore to leverage the input-dependent redundancy with dynamic quantization for different regions. However, the sensitive regions in the feature map are irregularly distributed, which restricts the real speed up for existing accelerators. To this end, we propose an algorithm-architecture co-design, named Structured Dynamic Precision (SDP). Specifically, we propose a quantization scheme in which the high-order bit part and the low-order bit part of data can be masked independently. And a fixed number of term parts are dynamically selected for computation based on the importance of each term in the group. We also present a hardware design to enable the algorithm efficiently with small overheads, whose inference time mainly scales with the precision proportionally. Evaluation experiments on extensive networks demonstrate that compared to the state-of-the-art dynamic quantization accelerator DRQ, our SDP can achieve 29% performance gain and 51% energy reduction for the same level of model accuracy.

Deep neural networks have achieved remarkable advancement in various intelligence tasks. However, the massive computation and storage consumption limit applications on resource-constrained devices. While channel pruning has been widely applied to compress models, it is challenging to reach very deep compressions for such a coarse-grained pruning structure without significant performance degradation. In this article, we propose an acceleration-aware fine-grained channel pruning (AFCP) framework for accelerating neural networks, which optimizes trainable gate parameters by estimating residual errors between pruned and original channels with hardware characteristics. Our fine-grained concept consists of both algorithm and structure levels. Different from existing methods that leverage a predefined pruning criterion, AFCP explicitly considers both zero-out and similar criteria for each channel, and adaptively selects the suitable one via residual gate parameters. For structure level, AFCP adopts a fine-grained channel pruning strategy for residual neural networks and a decomposition-based structure, which further extends the pruning optimization space. Moreover, instead of using theoretical computation costs, such as floating-point operations, we propose the hardware predictor that bridges the gap between realistic acceleration and pruning procedure to guide the learning of pruning, which improves the efficiency of model pruning when deployed on accelerators. Extensive evaluation results demonstrate that AFCP outperforms state-of-the-art methods, and achieves a favorable balance between model performance and computation cost.