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.
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