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- A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
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Description
Developing a RISC-V instruction set architecture (ISA) extensions for neuromorphic computing in particular for event-based DNNs using minifloats. Recent 8-bit foating-point (minifoat) representations used by DNNs have achieved marginal equivalent accuracy to FP32 foating point precision over different tasks and datasets while providing orders of magnitude reduction in silicon area and power consumption. Minifoats are ideal candidates for neuromorphic computing. The RISC-V core will hand all the maintenance and operations code, perform math operations that the neuromorphic signal processor cannot, and provide various other functions. Essentially, the RISC-V core will serve as a management node for the neuromorphic signal processor. This will greatly ease the use of the neuromorphic signal processor, since it will allow end users with limited knowledge of neuromorphic computing to develop AI applications.
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- Task-Driven Model Evaluation in Large-Scale Spiking Neural Networks
- A Neuromorphic Ferroelectric field-effect Ultra-Scaled Chip for Spiking Neural Networks
- Event Based Wavefront Sensing Modalities
- Physics-Based Encoding for Spiking Neural Networks
- Neuromorphic Computational Imaging
- Defining Performance Metrics for Closed Loop Event Based Imaging Systems
- A Neuromorphic Framework for Event-Based DNNs using Minifloats
- A RISC-V instruction set architecture (ISA) extensions for neuromorphic computing using minifloats
- Astrometry with Event-based Vision Sensors
- Automatic Evaluation of Bushfire Risk via Acoustic Scene Analysis
- Bio-inspired Sensors for Space Situational Awareness
- Building a Neuromorphic Auditory Pathway for Sensing the Surrounding Environment
- Cold Start Astrometry for High-Precision Airspace and Space Objects Tracking with Neuromorphic Cameras
- Control Systems Inspired by Insect Central Pattern Generators that can Adapt to Dynamic Environments.
- Design of Neuromorphic Spiking Neural Networks for Real-Time Processing
- Enhanced Maritime Situational Awareness with Neuromorphic Cameras
- Environmental Situational Awareness using Neuromorphic Vision Sensors and IMU-based SLAM
- Fault Tolerant Distributed Swarm Intelligence using Neuromorphic Computing and Local Learning Principles
- Honey Bee Waggle Dance Detection via Neuromorphic Engineering
- Integrated Circuit Design for Event-based Vision Sensors
- Low-Power Acoustic Ecological Monitoring in Remote Areas using Machine Learning and Neuromorphic Engineering
- Neuromorphic Computing in Extreme Environments
- Neuromorphic Cyber Security at the Edge
- Neuromorphic Engineering for Acoustic Aerial Drone Detection in Visually Obscured Environments
- Machine Learning-Based Tool for Therapists to Monitor Speech Progress in Late Talkers
- Machine Learning for Automated Child Reading Assessment and Intervention
- Underwater Acoustic Drone Detection via Neuromorphic Models of Marine Mammal Audition
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