arxiv_ai 70% Match Research Paper Hardware Engineers,AI System Designers,Embedded Systems Developers 20 hours ago

A High-Throughput Spiking Neural Network Processor Enabling Synaptic Delay Emulation

robotics › embodied-agents

📄 Abstract

Abstract: Synaptic delay has attracted significant attention in neural network dynamics for integrating and processing complex spatiotemporal information. This paper introduces a high-throughput Spiking Neural Network (SNN) processor that supports synaptic delay-based emulation for edge applications. The processor leverages a multicore pipelined architecture with parallel compute engines, capable of real-time processing of the computational load associated with synaptic delays. We develop a SoC prototype of the proposed processor on PYNQ Z2 FPGA platform and evaluate its performance using the Spiking Heidelberg Digits (SHD) benchmark for low-power keyword spotting tasks. The processor achieves 93.4% accuracy in deployment and an average throughput of 104 samples/sec at a typical operating frequency of 125 MHz and 282 mW power consumption.

Key Contributions

Introduces a high-throughput SNN processor with synaptic delay emulation for edge applications. The processor uses a multicore pipelined architecture and achieves efficient real-time processing, demonstrated on an FPGA prototype for low-power keyword spotting.

Business Value

Enables more sophisticated AI capabilities on low-power edge devices, such as voice assistants or sensor analysis, without constant cloud connectivity.

Paper Metadata

Innovation Type

Hardware Architecture Design

Deployment Feasibility

High, demonstrated on an FPGA prototype for edge applications.

Limitations Addressed

Computational load of synaptic delays in SNNs; need for low-power, high-throughput processing at the edge.

Performance Gains

Achieves 93.4% accuracy, 104 samples/sec throughput at 125 MHz and 282 mW power consumption.

Technical Tags

Spiking Neural Networks (SNN)Synaptic Delay EmulationHigh-Throughput ProcessorSoC PrototypeFPGAEdge ComputingKeyword SpottingLow-Power Computing

Research Topics

Neuromorphic ComputingHardware Accelerators for AIEdge AIEfficient Neural Networks

Methods & Architectures

Spiking Neural Network (SNN) processingMulticore pipelined architectureParallel compute enginesSynaptic delay emulation Spiking Neural Network (SNN)

Applications & Tasks

Edge Computing Internet of Things (IoT) Embedded Systems Speech Processing Efficient spatiotemporal information processingLow-power AI inference at the edge Keyword spottingReal-time signal processing

Datasets & Benchmarks

Datasets

Spiking Heidelberg Digits (SHD)

Benchmarks

SHD accuracy: 93.4%

AccuracyThroughputPower Consumption

Related Fields

Neuromorphic EngineeringComputer ArchitectureEmbedded SystemsSignal Processing

Keywords

Spiking Neural NetworksSNNSynaptic DelayHardware AcceleratorFPGAEdge AIKeyword SpottingLow PowerSoCNeuromorphic Computing

Academic Context

#Neuromorphic Computing#Hardware Accelerators for AI#Edge AI#Efficient Neural Networks

Technology Stack

Frameworks & Libraries

PYNQ Z2

Commercial Potential

Potential Products

Low-power AI chips for IoT devicesEdge AI modules for smart sensors

Target Industries

Consumer ElectronicsAutomotiveIndustrial Automation

Use Case Examples

Always-on voice command detection in smart home devicesReal-time anomaly detection in industrial sensors

Competitive Edge

Offers a specialized hardware solution for SNNs with synaptic delay emulation, targeting efficiency and throughput for edge applications.

Market Opportunity

Growing market for edge AI and IoT devices.

Revenue Models

Hardware saleslicensing of IP.

Resource Requirements

Compute Needs

Specific to the SoC prototype on FPGA, low power consumption.

Data Requirements

Requires datasets suitable for SNNs and keyword spotting tasks.

Deployment Constraints

FPGA-based, potentially limited by hardware availability and specific application needs.

Scalability

Multicore pipelined architecture suggests scalability.

Production Readiness

Maturity Level

Prototype/Research

Time to Market

1-3 years for commercialization.

Patent Potential

High, for the novel processor architecture.

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