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[1/14] Excited to share that our neuromorphic chip ReckOn made it to #ISSCC! With this spiking RNN processor, we demonstrate for the first time end-to-end on-chip learning over second-long timescales.
Tune in to Session 29 on Feb 24 9am PST for the live Q/A... or see π for a π§΅
Tune in to Session 29 on Feb 24 9am PST for the live Q/A... or see π for a π§΅
[2/14] First, why on-chip learning? For continuous adaption to new/uncontrolled user and environment features, evolving task requirements, and privacy. However, no end-to-end solution has been proposed to date for on-chip learning with dynamic stimuli such as speech/video.
[3/14] Indeed, backpropagation through time (BPTT) requires unrolling the network in time during training. Learning second-long stimuli while keeping a milli-second resolution would thus lead to intractable memory and latency requirements for tiny silicon devices at the edge.
