Neural Computing-Driven Signal Processing Frameworks for IoT-Enabled AR/VR and Robotic Systems
The article presents a very large-scale integration (VLSI)-based neural signal processing system that is meant to provide high-performance, low-latency, and energy-efficient computations to realize next-generation IoT-enabled augmented and virtual reality (AR/VR) and robotics. The architecture sugge...
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| Other Authors: | , , , , , |
| Format: | article |
| Published: |
2025
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| Online Access: | http://hdl.handle.net/11675/14498 https: |
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| Summary: | The article presents a very large-scale integration (VLSI)-based neural signal processing system that is meant to provide high-performance, low-latency, and energy-efficient computations to realize next-generation IoT-enabled augmented and virtual reality (AR/VR) and robotics. The architecture suggested combines the hardware/software co-design, neural model compression, and scalable VLSI implementation to facilitate real-time on-device intelligence. Fundamentally, the architecture has an adaptive multistage pipeline that integrates multimodal sensor data vision, motion, and environmental streams via a hybrid neural signal processing stack composed of convolutional, recurrent, and spiking neural modules. In contrast to traditional DSP or entirely algorithmic accelerators, the system is based on VLSI-conscious neural mapping, dataflow scheduling, and precision-adaptive arithmetic to reduce the computation latency and power consumption with a rigid set of edge resources. Designed on a reconfigurable FPGA-VLSI platform, the design has shown significant benefits in a variety of AR/VR and robotic metrics with the lowest system latency, throughput, and energy consumption of up to 3.2×, 2.7×, and 58%, respectively, over initial DSP and classical processing designs. These findings affirm that the framework is a single, extensible platform of real-time signal-driven intelligence, which can be developed to enhance immersive, autonomous, and edge-sensitive computing platforms in smart robotics, wearable systems, and cyber-physical environments. |
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