2021 BRN Discussion, page-2280

The important claims for the PCT application:

1. A system, comprising:

a spike converter configured to generate spikes from the digital input data;

an inbound filter configured to select relevant spikes from the generated spikes,

wherein the relevant spikes are relevant to a neuron;

a memory configured to store kernels in inverted format, and further configured to store weights indexed by channel;

a packet collection module configured to collect the relevant spikes until a predetermined number of relevant spikes have been collected in a packet in memory, and

to organize the collected relevant spikes by channel and spatial coordinates in the packet; and

a convolution neural processor configured to perform row-by-row strides in the memory, the convolution neural processor using a scratchpadmemory within the memory,

wherein the inbound filter removes events outside a scopeof the convolution neural processor.

11. A method, comprising:

receiving digital input data;

generating, by a spike converter, spikes from the digital input data;

selecting, by an inbound filter, relevant spikes from the generated spikes;

storing kernels in a memory in inverted format,

the storing further including storing weights indexed by channel;

collecting the relevant spikes until a predetermined number of relevant spikes have been collected in a packet in memory;

organizing the collected relevant spikes by channel and spatial coordinates in the packet; and

performing, using a convolution neural processor and a scratchpad memory, a convolution in hardware using row-by-row strides in the memory.

13. The method of claim 11, further including performing a learning algorithm that limits learning to one neuron per filter.