4 ms·
But is it really much more complicated? It looks very simple to me. Maybe actually even simpler than such state machine?
by albertzeyer 3y ago
But is it really much more complicated? It looks very simple to me. Maybe actually even simpler than such state machine?
- thesz 3y agoThere are several multipliers and adders (possible, floating point ones), a register file for perceptron weights and perceptron selection logic. The scheme also need to keep information somewhere for training - the decision and backpropagation stimulus are separeted in time. So the circuitry is complicated despite superficial simplicity of the model.
- isotypic 3y agoThe version in the paper only is ever multiplying by 1 or -1, so you do not need a full multiplier circuit. The weights are also stored as signed integers, not floats, so no complicated floating point circuity. I am not sure what current state of the art is, but considering the cost of multipliers/floating point circuity I would be surprised if it changed to those if signed integers work. All branch predictors need some way of storing their state and selection logic, and the way a perceptron branch predictor stores its data is just a big table indexed by some hash of the program counter of the branch, which is pretty standard for branch predictors. Also, all branch predictors have a sort of "backpropogation" in that pipelined processors produce the actual result of the branch (possibly many) cycles later, so this also is not as much of a factor. Since the training is a function of the weights you do not need to store extra data beyond the threshold, but that is already being computed as the prediction anyways.