AI Hardware & Software Stack
What is the main reason neuromorphic computing hardware can aim for low power consumption?
- AAll neurons always run simultaneously at a high clock frequency
- BAll calculations are performed in 64-bit floating point
- CIt is event-driven, so only the parts that receive spikes are active, and memory holding the weights sits close to computation to reduce data movement
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Answer: C. It is event-driven, so only the parts that receive spikes are active, and memory holding the weights sits close to computation to reduce data movement
Neuromorphic computing takes the brain's neural circuits as a model and conveys information as pulses called spikes. Only the parts that receive spikes when input changes are active, so in sparse workloads idle circuits use almost no power. Synaptic weights are held in memory inside each core, reducing long data movement to and from arithmetic units. These features distinguish it from GPUs, which perform dense matrix operations in lockstep.
- A. Running everything in lockstep all the time would increase power.
- B. High-precision number formats push power up, not down.
- C. Correct. Event-driven operation and memory close to computation are the reasons.