Spiking Neural Network Search Under Neuromorphic Hardware Constraints
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Solution Overview
Problem
Existing methods for generating Spiking Neural Networks (SNNs) for neuromorphic devices face challenges such as complexity in neuron models, training and inference inefficiencies, model selection difficulties, computational inefficiencies, and hardware implementation issues, leading to sub-optimal performance and resource constraints.
Innovation Solution
A method and system utilizing Reinforcement Learning (RL) Neural Architecture Search (NAS) with constrained search spaces and Faster-NAS techniques to generate optimized SNNs, incorporating neuromorphic hardware constraints and optimizing Neuromorphic Processing Unit (NPU) utilization, enabling efficient deployment on target devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If handcrafted SNN models are used, then model accuracy can be optimized, but the complexity of neuron models and training procedures increases significantly
Solution Approach 1:
The system employs automated neural architecture search that enables the model to self-optimize its structure and parameters without extensive manual intervention. The reinforcement learning agent automatically explores the search space and selects optimal configurations, reducing the burden of handcrafting complex neuron models while maintaining high accuracy.
Solution Approach 2:
The system dynamically adjusts neuron model parameters and architecture configurations during the search process. By changing parameters such as neuron types, layer structures, and connectivity patterns, the system finds optimal configurations that balance accuracy with computational efficiency and hardware constraints.
2Extent of automation
If conventional ANN2SNN conversion methods are used, then automation is introduced, but conversion errors and information loss increase
Solution Approach 1:
The system implements a feedback mechanism where the reinforcement learning agent evaluates the performance of converted SNN models and uses this information to refine the conversion process. The agent learns from conversion outcomes and adjusts its strategy to minimize information loss and conversion errors in subsequent iterations.
Solution Approach 2:
The system performs preliminary optimization of the ANN model before conversion to SNN. By pre-training the ANN and carefully preparing the weight initialization and architecture configuration beforehand, the system reduces conversion errors and information loss during the transformation process.
3Extent of automation
If traditional ANN2SNN methods are used, then automation is achieved, but performance at low latency deteriorates
Solution Approach 1:
The system dynamically optimizes SNN architecture parameters during the search process to achieve the best performance-latency tradeoff. The reinforcement learning agent explores different configurations and selects those that provide high inference speed while maintaining accuracy, adapting to specific hardware constraints and application requirements.
4Measurement precision
If extensive SNN search is performed to find optimal models, then model performance improves, but search time and resource utilization increase
Solution Approach 1:
The system performs a limited but targeted search through the SNN configuration space using reinforcement learning. Rather than exhaustively searching all possible configurations, the agent focuses on promising regions of the search space, achieving good performance with reduced search time by exploring only the most relevant architectural options.
Solution Approach 2:
The system conducts preliminary filtering and pruning of the search space before performing detailed optimization. By pre-identifying promising architectural patterns and constraints based on hardware requirements and application characteristics, the system reduces the overall search time while still finding high-performance models.
Data Source
AI summary
State of art techniques such as ANN2SNN yield sub-optimal performance for Spiking Neural Networks (SNNs), while with SNN search space Neural Architecture Search (NAS) based approach many times the target SNN hardware constraints might not be met or there can be accuracy loss. A method and system for rapid automated generation of optimized SNNs for neuromorphic devices is disclosed. A Reinforcement Learning (RL)-NAS technique is utilized to obtain Deep Neural Networks (DNNs) using raw training dataset in non-spike data format and set of SNN constraints in accordance with the neuromorphic hardware of target neuromorphic devices along with general target hardware constraints, wherein the set of constraints are imbibed into the NAS space in form of constrained graph. An optimal DNN is model searched in an SNN contained NAS space and trained on the raw data. An optimal SNN model is obtained from the trained DNN model via neuromorphic OEM converter.