Nervous System on Chip HDL Translation for Neuromorphic FPGA
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Solution Overview
Problem
Existing technologies face challenges in accurately modeling and simulating nervous systems at microchip speeds, lacking the ability to integrate disparate input types and operate at the level of biological realism, especially on reconfigurable FPGA hardware.
Innovation Solution
The method involves translating nervous system models into Hardware Description Language (HDL) and converting floating-point arithmetic to fixed-point arithmetic, enabling the simulation and hardware implementation of neuron and astrocyte modules on FPGA or ASIC devices, allowing for parallel operation of neurological entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If nervous system models are implemented on traditional computing devices, then computational flexibility is maintained, but operating speed and parallelism are insufficient to achieve microchip speeds
Solution Approach 1:
The patent replaces traditional software-based nervous system simulations on general-purpose computers with hardware-based implementations using FPGAs and ASICs. This substitution of mechanical/computational systems with dedicated hardware circuits enables microchip-speed operation while maintaining the ability to model complex nervous system dynamics through reconfigurable logic elements and parallel processing architectures.
Solution Approach 2:
The nervous system model is divided into discrete, independently implementable components including neuron modules, synapse modules, and glial cell modules. Each component is implemented as separate hardware units that can be instantiated and interconnected on FPGA devices, enabling parallel operation while simplifying the overall implementation through modular design.
2Measurement precision
If floating-point arithmetic is used for accurate neural computation, then computational precision is improved, but hardware resource consumption and implementation cost increase
Solution Approach 1:
The patent transitions from floating-point arithmetic to fixed-point arithmetic for neural computations. This parameter change in the numerical representation method maintains sufficient computational precision for neuroscience applications while dramatically reducing hardware resource requirements, eliminating the need for complex floating-point units, and simplifying FPGA and ASIC implementation.
3Reliability
If comprehensive nervous system models with biological realism are implemented, then modeling accuracy is improved, but device complexity and resource requirements increase
Solution Approach 1:
The comprehensive nervous system model is segmented into distinct functional modules representing different cell types (neurons, astrocytes, oligodendrocytes, microglia) and subcellular components (dendrites, axons, synapses). Each module is implemented as independent hardware units with standardized interfaces, enabling accurate biological modeling while managing complexity through modular architecture and parallel instantiation.
Solution Approach 2:
The patent implements universal neuron and synapse modules that can be instantiated multiple times with different parameters to represent various cell types and connection patterns. This multi-functional design allows a single hardware module to model diverse neural elements, reducing overall system complexity while maintaining comprehensive biological realism through parameter configuration rather than separate dedicated hardware for each cell type.
Data Source
AI summary
A method to translate a nervous system model into Hardware Description Language (HDL) is presented here. The nervous system model is that produced from the Nervous System Modeling Tool, patent application Ser. No. 15/660,858, and the HDL translation downloads into either a Field Programmable Gate Array (FPGA) chip or an Application-Specific Integrated Circuit (ASIC) architecture. The method supports the neurobiological realism of Ser. No. 15/660,858 and adds massive parallelism operating at adjustable microchip speeds. A neurobiologically realistic nervous system embedded on a microchip achieves the goal of neuromorphic computing and thus embodies a nervous system on a chip. The potential applications are extensive and cover the range of robotics, big data analysis, medical diagnostics and remediation, self-learning systems, and artificially intelligent applications such as intelligent assistants. Intelligent assistants can be applied to the fields of language and technology exposition, the Internet of Things (IOT) and security.