Integrated Event-Based SoC: Revolutionizing Sensor and AI Processor Performance with Low-Latency, Energy-Efficient Neuromorphic Computing
Lead Participant:
RIGPA LTD
Abstract
Our project focuses on creating an innovative System-on-Chip (SoC) design that integrates an event-based photodetector sensor with a neuromorphic processor. This approach tackles the latency, energy inefficiency, and complexity issues commonly found in traditional sensor and processor architectures. The SoC design employs advanced chiplet technology for scalable and cost-effective fabrication, optimizing performance and cost.
The integrated SoC solution offers several key advantages:
1. Enhanced Latency and Energy Efficiency: By merging the sensor and processor into one module, our design reduces latency and energy consumption. The event-based sensor captures data only when significant events occur, further decreasing energy usage and data processing requirements.
2. Advanced Neuromorphic Processing: The processor is designed to imitate biological neural networks, allowing efficient, low-power processing of sensor data. This architecture is particularly suitable for edge computing applications, where low latency and energy efficiency are crucial.
3. Scalable Chiplet Technology: Utilizing cutting-edge chiplet packaging technology enables us to fabricate the SoC design at different process nodes, striking a balance between performance, energy efficiency, and affordability.
Our SoC design targets a wide range of applications, including autonomous vehicles, robotics, IoT devices, and smart city infrastructure, addressing the growing demand for high-performance, energy-efficient sensors and processors. With the edge computing market expected to exceed $15 billion by 2025, our innovative SoC solution is well-positioned to capture a significant share of this expanding market.
Offering a unique, affordable, and energy-efficient solution, our project aims to reshape the sensor and processor landscape, providing a cutting-edge alternative to traditional architectures. Upon completion, our integrated SoC design will contribute to the development of more efficient, responsive, and sustainable technologies, driving advancements in various sectors and improving the overall quality of life.
The integrated SoC solution offers several key advantages:
1. Enhanced Latency and Energy Efficiency: By merging the sensor and processor into one module, our design reduces latency and energy consumption. The event-based sensor captures data only when significant events occur, further decreasing energy usage and data processing requirements.
2. Advanced Neuromorphic Processing: The processor is designed to imitate biological neural networks, allowing efficient, low-power processing of sensor data. This architecture is particularly suitable for edge computing applications, where low latency and energy efficiency are crucial.
3. Scalable Chiplet Technology: Utilizing cutting-edge chiplet packaging technology enables us to fabricate the SoC design at different process nodes, striking a balance between performance, energy efficiency, and affordability.
Our SoC design targets a wide range of applications, including autonomous vehicles, robotics, IoT devices, and smart city infrastructure, addressing the growing demand for high-performance, energy-efficient sensors and processors. With the edge computing market expected to exceed $15 billion by 2025, our innovative SoC solution is well-positioned to capture a significant share of this expanding market.
Offering a unique, affordable, and energy-efficient solution, our project aims to reshape the sensor and processor landscape, providing a cutting-edge alternative to traditional architectures. Upon completion, our integrated SoC design will contribute to the development of more efficient, responsive, and sustainable technologies, driving advancements in various sectors and improving the overall quality of life.
Lead Participant | Project Cost | Grant Offer |
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Participant |
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RIGPA LTD |
People |
ORCID iD |
Xiaoyu Huang (Project Manager) |