Analog Computing

Achieving unmatched efficiency and performance


Analog computing provides the ultimate compute-in-memory processing element. The term compute-in-memory is nuanced  . Our analog compute elevates compute-in-memory by computing directly inside the memory array. This is possible by using memory elements as tunable resistors, supplying the inputs as voltages, and collecting the outputs as currents. We use analog computing for our core neural network matrix operations, where we multiply an input vector by a weight matrix.

Analog computing provides several key advantages:

  • First, it is amazingly efficient; it eliminates memory movement for the neural network weights since they are used in place as resistors.
  • Second, it is high performance; there are hundreds of thousands of multiply-accumulate operations occurring in parallel when we perform one of these vector operations.

Given these two properties, analog computing is the core of our high-performance yet highly-efficient system.