SYNAPSEHardware That Thinks
Like Tissue.
Silicon spiking neural networks · 0.8 mW inference · Biological-scale plasticity
The Von Neumann bottleneck imposes a 14× energy overhead at the edge. Every inference cycle on a conventional GPU requires data to travel the memory bus repeatedly — a structural inefficiency that compounds under continuous workloads.
The SN-7 eliminates this by co-locating memory and computation within each synaptic core. Spike-timing-dependent plasticity (STDP) runs natively in silicon: weights update in 0.4 µs without host intervention.
Benchmark results across 12 edge inference tasks show a median latency of 3.2 ms at 0.8 mW — against 48 ms at 93 mW for the nearest cloud-GPU baseline. The delta holds across gesture recognition, SLAM localization, and spike-based object detection.
On-device learning without cloud round-trips. Autonomous systems that adapt to new environments in under 200 ms. Hardware that speaks the same event-driven language as the models running on it.
A single spike traverses 7 synaptic layers in 1.96 ms
Perception without the power budget.
Prototype edge inference on manipulators and mobile platforms where battery life is non-negotiable. SN-7 runs gesture classification, optical flow, and depth estimation simultaneously at under 2 mW total.
Low-power autonomy at the tactical edge.
DARPA-validated for denied-environment operation. No RF signature from cloud round-trips. On-device learning adapts to new threat signatures in under 200 ms.
Hardware that speaks your model's language.
Run NEST-compatible SNN models directly on silicon. Biological spike timing, STDP, and homeostatic plasticity — no translation layer.
Deploy your SNN on real
neuromorphic silicon.
Our cloud sandbox gives researchers direct access to SN-7 hardware. Deploy small spiking neural network models, measure real power draw, verify your latency numbers against our published benchmarks.
Full technical comparison: power, latency, spike-timing accuracy, and STDP learning curves across 12 workload categories.
"The brain uses 20 watts to run everything you've ever thought.
We're building the silicon that earns that comparison."