Boab
I wish I could paint like Vincent
Oh yes, this gives us a very nice plug.
The following is the paragraph that precedes @Diogenese earlier post.
Event-based Processing & Learning: BrainChip’s Neuromorphic AI Solution
BrainChip is one of the pioneers of bringing neuromorphic computing to the edge. While traditional neuromorphic approaches have used analog designs to mimic the neuron and synapse, BrainChip has taken a novel approach on three counts.
Firstly, their design is a fully digital design that is portable and reliable.
Secondly, not only do they support spiking neural nets, but they have applied event-based execution to traditional convolutional networks, thereby rendering neuromorphic computing mainstream today. This allows current CNN/RNN models to run much more efficiently and drives far more capable performance on extremely low-footprint, low-power devices at the sensor.
Thirdly, delivering on-device learning allows for personalization, customization, and other learning untethered from the cloud.
Brainchip’s Akida neural processor is offered as IP and is configurable from energy-harvesting applications at the sensor edge to high-performance yet power-efficient solutions at the network edge. It is sensor-agnostic and has been demonstrated on a variety of sensors.
As a self-managed neural processor that executes most networks completely in hardware without CPU intervention, it addresses key congestion and system bandwidth challenges in embedded SoCs while delivering highly efficient performance. With support for INT8 down to INT1 and skip connections, it handles most complex networks today, along with spiking neural nets.
