Huawei Wang

Vittorio Caggiano, Huawei Wang, Guillaume Durandau et al.

Embodied agents in continuous control domains have had limited exposure to tasks allowing to explore musculoskeletal properties that enable agile and nimble behaviors in biological beings. The sophistication behind neuro-musculoskeletal control can pose new challenges for the motor learning community. At the same time, agents solving complex neural control problems allow impact in fields such as neuro-rehabilitation, as well as collaborative-robotics. Human biomechanics underlies complex multi-joint-multi-actuator musculoskeletal systems. The sensory-motor system relies on a range of sensory-contact rich and proprioceptive inputs that define and condition muscle actuation required to exhibit intelligent behaviors in the physical world. Current frameworks for musculoskeletal control do not support physiological sophistication of the musculoskeletal systems along with physical world interaction capabilities. In addition, they are neither embedded in complex and skillful motor tasks nor are computationally effective and scalable to study large-scale learning paradigms. Here, we present MyoSuite -- a suite of physiologically accurate biomechanical models of elbow, wrist, and hand, with physical contact capabilities, which allow learning of complex and skillful contact-rich real-world tasks. We provide diverse motor-control challenges: from simple postural control to skilled hand-object interactions such as turning a key, twirling a pen, rotating two balls in one hand, etc. By supporting physiological alterations in musculoskeletal geometry (tendon transfer), assistive devices (exoskeleton assistance), and muscle contraction dynamics (muscle fatigue, sarcopenia), we present real-life tasks with temporal changes, thereby exposing realistic non-stationary conditions in our tasks which most continuous control benchmarks lack.

Zihan Jiang, Fanghao Liu, Huawei Wang et al.

Computational complexity and storage requirements are crucial factors influencing the performance and efficiency of convolutional neural networks (CNNs) in resource-constrained environments. This paper presents a high-performance embedded target detection system based on FPGA and YOLOv3-Tiny, specifically designed for embedded artificial intelligence applications. By integrating lightweight CNN optimization techniques with hardware accelerator design, significant improvements are made in both computational efficiency and resource utilization. Key optimizations, including low-bit quantization, batch normalization fusion, and table lookup mapping, reduce model parameters and computational complexity. Additionally, an FPGA hardware accelerator with a pipelined architecture is developed to enhance the efficiency of convolution operations while minimizing off-chip data transmission through modular design and on-chip cache optimization. On the ZYNQ-XC7Z035 platform, the system achieves an inference latency of 0.211 seconds, outperforming comparable designs by 75.58% in speed. The system achieves an power efficiency of 10.11 GOPS/W, surpassing comparable designs by at least 29.45%. Furthermore, hardware resource utilization is reduced by up to 51.94% compared to similar systems. This study offers innovative design methodologies and practical application examples for the efficient deployment of deep learning models on embedded platforms.