hardware-aware-mixed-signal-snn-framework

name	hardware-aware-mixed-signal-snn-framework
description	Hardware-aware open-source framework for mixed-signal Spiking Neural Network design space exploration. Captures non-ideal analog/digital hardware behavior while supporting system-level exploration for energy-efficient neuromorphic edge computing. Activation: mixed-signal SNN, hardware-aware simulation, design space exploration, neuromorphic edge, non-ideal hardware modeling, SNN accelerator
tags	["mixed-signal","hardware-aware","SNN","design-space-exploration","neuromorphic","edge-computing"]
metadata	{"arxiv_id":"2607.06456","published":"2026-07-07","authors":"Sayma Nowshin Chowdhury, Vineeta Nair, Taseen Forhad, et al.","categories":"eess.SP, cs.NE"}

Hardware-Aware Mixed-Signal SNN Framework for Design Space Exploration

Core Concept

Energy-efficient neuromorphic computing at the edge requires simulation tools that capture the non-ideal behavior of mixed-signal (analog + digital) Spiking Neural Network hardware while supporting system-level design exploration. This framework provides an open-source platform for exploring the trade-off space of mixed-signal SNN accelerators, modeling hardware non-idealities (noise, mismatch, quantization) and their impact on neural network accuracy and energy efficiency.

Key Innovations

1. Mixed-Signal Hardware Modeling

• Analog components: Memristive crossbars, analog integrators, voltage-controlled oscillators
• Digital components: Event routers, spike buffers, control logic
• Non-ideal effects: Device mismatch, thermal noise, finite precision, nonlinearity
• Process-voltage-temperature (PVT) variation: Corner-case analysis

2. Design Space Exploration

• Architecture parameters: Neuron model type, synaptic precision, routing topology
• Precision levels: 4-bit, 8-bit, mixed-precision configurations
• Energy-accuracy trade-offs: Pareto frontier identification
• Scalability analysis: Performance as network size increases

3. Open-Source Framework

• Modular architecture: Pluggable neuron/synapse models
• Hardware-accurate simulation: Bit-level or cycle-accurate emulation
• Benchmark integration: Standard neuromorphic benchmarks
• Extensible API: Custom hardware models and algorithms

Hardware Non-Idealities Modeled

Analog Domain

Non-Ideality	Description	Impact
Device mismatch	Parameter variation between nominally identical components	Weight precision degradation
Thermal noise	Johnson-Nyquist noise in resistive components	Spike timing jitter
Finite precision	Limited bit-width for weights and activations	Quantization error
Nonlinearity	Non-linear device characteristics	Activation function distortion
Leakage current	Subthreshold leakage in analog circuits	Membrane potential drift
Supply noise	IR-drop and ground bounce	Threshold voltage variation

Digital Domain

Non-Ideality	Description	Impact
Finite buffer depth	Limited spike queue capacity	Spike loss under high activity
Routing latency	Event propagation delay	Temporal precision loss
Clock skew	Clock distribution variation	Synchronization errors
Memory bandwidth	Limited memory access rate	Throughput bottleneck

Design Space Parameters

Neuron Model Selection

• Leaky Integrate-and-Fire (LIF): Simple, low-power
• Adaptive Exponential (AdEx): Biologically realistic, moderate complexity
• Izhikevich: Rich dynamics, higher compute cost
• Custom: User-defined neuron dynamics

Precision Configurations

• Full precision: 32-bit floating point (baseline)
• Mixed precision: 8-bit weights + 16-bit activations
• Low precision: 4-bit weights + 4-bit activations
• Binary: 1-bit weights and activations

Architecture Variants

• Fully analog: Maximum energy efficiency, lowest precision
• Hybrid analog-digital: Balanced performance and accuracy
• Fully digital: Highest precision, maximum energy cost

Methodology

Step 1: Define Target Application

• Select benchmark dataset (e.g., N-MNIST, DVS-Gesture)
• Define accuracy requirements
• Set energy/power budget

Step 2: Configure Hardware Model

• Choose neuron/synapse models
• Set precision levels
• Configure non-ideality parameters
• Define routing architecture

Step 3: Simulate and Evaluate

• Run hardware-accurate simulation
• Measure accuracy vs. ideal software baseline
• Estimate energy consumption
• Identify bottlenecks

Step 4: Explore Design Space

• Sweep key parameters systematically
• Generate Pareto frontier plots
• Identify optimal configurations
• Report energy-accuracy trade-offs

Applications

Edge AI Deployment

• Always-on wake-word detection
• Event-based vision processing
• Low-power sensor classification

Neuromorphic Hardware Design

• Architecture exploration before fabrication
• Parameter tuning for specific workloads
• Trade-off analysis for target applications

Algorithm-Hardware Co-Design

• Joint optimization of algorithms and hardware
• Quantization-aware training
• Hardware-constrained network search

Pitfalls

Simulation vs. Silicon Gap

Problem: Simulation accuracy may not match real hardware behavior Solution: Validate against known silicon measurements, use conservative margins

Exploration Complexity

Problem: Design space grows exponentially with parameters Solution: Use response surface methodology, surrogate models, or Bayesian optimization

Non-Ideality Calibration

Problem: Non-ideality parameters may not match target process technology Solution: Use process design kit (PDK) data, characterize test chips

Accuracy Degradation

Problem: Aggressive quantization or noise may cause unacceptable accuracy loss Solution: Use mixed-precision strategies, apply noise-aware training

Validation Metrics

Accuracy Metrics

• Classification accuracy vs. software baseline
• Degradation under hardware non-idealities
• Worst-case accuracy across PVT corners

Energy Metrics

• Energy per inference (pJ/inference)
• Energy-delay product
• Power efficiency (inferences/Joule)

Area Metrics

• Chip area estimation (mm²)
• Transistor count
• Memory footprint

Implementation Considerations

Software Stack

• Python-based configuration API
• Fast C/C++ simulation backend
• Integration with PyTorch/TensorFlow for training
• Visualization tools for results analysis

Benchmarking

• Standard neuromorphic datasets
• Custom workload injection
• Comparative analysis with baselines
• Reproducible experiment configuration

References

• Paper: arXiv:2607.06456 (July 7, 2026)
• Authors: Sayma Nowshin Chowdhury, Vineeta Nair, Taseen Forhad, et al.