Without the marketing layer

Neuromorphic hardware is easy to discuss in slogans and harder to integrate into a real pipeline. For our work, the interesting question is not whether a processor is “neuromorphic.” It is whether a particular model maps, runs predictably, and creates an advantage in the constraints that actually matter at the edge.

Mapping becomes part of system design

On Akida, a model is not just a file you hand to a generic accelerator. The runtime maps that model onto a hardware device. The summary can show how many hardware sequences are required, how NPs are allocated and how much external memory is involved. That makes hardware fit visible earlier in development.

Power claims need context

The Akida runtime can expose power-related measurements on supported hardware, but any number is inseparable from model, clock mode, input, mapping and board. We therefore treat vendor benchmarks as reference points and our own measurements as configuration-specific results.

Why we care at the edge

Our VORSA-M3 workcell has cameras, object detection, segmentation ambitions and mechanical systems that have to react in a bounded physical environment. That makes latency, power and local processing interesting — but only if the complete pipeline is stable enough to operate.

Our rule: reproduce before generalizing

We will publish three kinds of information: vendor baselines, reproduced observations and experiments. Keeping those labels visible is more useful than pretending every prototype result is a product specification.