DYNAP-CNN

The world’s first fully scalable, event-driven neuromorphic processor with up to 1M configurable spiking neurons and direct interface with external DVS.

DYNAP-CNN is a fully scalable, event-driven neuromorphic processor with up to 1M configurable spiking neurons and direct interface with external DVS.This technology is ideal for always-on, ultra-low power and ultra-low latency event-driven sensory processing applications. With a dedicated interface for dynamic-vision-sensors, it allows direct input of event streams from most of the advanced dynamic-vision-senors in the world, enabling seamless integration and rapid prototyping of models. DYNAP-CNN is fully configurable and supports various types of CNN layers (like ReLU, Cropping, Padding and Pooling) and network models (like LeNet, ResNet and Inception). It provides complete control of your models with extensive programmablility of all of its parameters. In addition, DYNAP-CNN is scalable, enabling implementation of deep neural networks with unlimited number of layers over multiple interconnected DYNAP-CNNs.

Applications
  • Smart Toy
  • Smart Home
  • Smart Security
  • Autonomous Navigation
  • Drones

Scalable
Adaptive to event camera

Ultra-low latency
End-end latency of ms, 10-100x faster

Ultra-low power
100-1000x less power consumption ~ 1mW

Cost effective
Real time data processing, 10x less cost

Always-on
Event-driven computing, no more redundant power management system.

DYNAP-CNN DEVELOPMENT KIT

The DYNAP-CNN development kit is powered by SynSense DYNAP-CNN cores, which brings the flexibility of convolutional vision processing to milliwatt energy budgets. It provides the capabilities for real-time presence detection, real-time gesture recognition, real-time object classification, all with mW average energy use. The devkit supports event-based vision applications via direct input, or input via USB. Development of up to nine-layer convolutional networks is made easy with our open-source Python library SINABS.

Requirements
HardwareSoftwareTools
At least 4GB of physical memoryUbuntu 18.04/20.04SAMNA drive
Download
USB 3.0 type-A ×2ArchSINABS
Download
Display screenMac >= 10.15AERMANAGER
Equipped with an integrated/discrete graphics cardPython >= 3.5
DVS options:
Inivation DVXplorer Lite
Inivation Davis240
Inivation Davis346
HardwareSoftwareTools
At least 4GB of physical memoryUbuntu 18.04/20.04SAMNA drive
Download
USB 3.0 type-A ×2ArchSINABS
Download
Display screenMac >= 10.15AERMANAGER
Equipped with an integrated/discrete graphics cardPython >= 3.5
DVS options:
Inivation DVXplorer Lite
Inivation Davis240
Inivation Davis346
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