Windows Neuromorphic Co-Design Workspace
For device experiments, synapses, crossbars, SNNs, hardware metrics, variation/noise, Pareto optimization and the Engineering Agent.
NEUROMORPHIC HARDWARE · WINDOWS
Neuromorphic Hardware Co-Design Studio
From measured device data to synapses, crossbars, SNNs and system-level trade-offs
Place measured neuromorphic-device data and architecture analysis in one explainable workflow: fit the device, map it to synapses/crossbars/SNNs, then inspect latency, energy, throughput, area, variation and Pareto trade-offs.
Device I–VSynapseCrossbar VMMSNNTTFSADC / DACEnergy / NoCPareto / Variation
PLATFORMS
Platforms & Download
Each platform is designed for a different workflow: desktop for deeper engineering analysis and mobile for portable calculations and quick checks.
CAPABILITIES
Core Engineering Capabilities
The previous recording already covers the full path from device data to design trade-offs, so this page uses those real screens and results without requiring a new recording.
Device Data & Fitting
Import I-V, conductance, pulse, retention and endurance data, fit device models and inspect residuals.
Synapse & Crossbar
Map conductance to synaptic weights and compare ideal/non-ideal VMM with quantization, resistance and device noise.
SNN & Interface Circuits
SNN/Conv2D, rate coding, TTFS, ADC/DAC transfer and clipping/error analysis.
Hardware Metrics & Design Trade-offs
Latency, energy, throughput, area, TOPS/W, variation/noise, Pareto analysis and an optional Engineering Agent.
RESULTS & WORKFLOWS
Key screens from the real recorded/tested workflow
These are real application screens from the previous test workflow: device I-V, SNN architecture, ADC transfer and Pareto/robustness results.
PRODUCT POSITIONING
Who It Is For
For neuromorphic devices, compute-in-memory, SNN and hardware co-design research. Hardware metrics are engineering estimates under the configured analytical models and should be used alongside real device/chip measurements and independent validation.