Do read the whole thing, but here's the quick summary of most important parts: [neural net] “nodes” are just weights stored in a computer’s memory. Calculating

Do read the whole thing, but here's the quick summary of most important parts: [neural net] “nodes” are just weights stored in a computer’s memory. Calculating a dot product usually involves fetching a weight from memory, fetching the associated data item, multiplying the two, storing the result somewhere, and then repeating the operation for every input to a node... In the [new] chip, a node’s input values are converted into electrical voltages and then multiplied by the appropriate weights. Summing the products is simply a matter of combining the voltages. Only the combined voltages are converted back into a digital representation and stored for further processing. The chip can thus calculate dot products for multiple nodes — 16 at a time, in the prototype — in a single step, instead of shuttling between a processor and memory for every computation... One of the keys to the system is that all the weights are either 1 or -1... Recent theoretical work suggests that neural nets trained with only two weights should lose little accuracy — somewhere between 1 and 2 percent. Biswas and Chandrakasan’s research bears that prediction out. ... Their chip’s results were generally within 2 to 3 percent of the conventional network’s.