Branch Predicting with Sparse Distributed Memories

Understand

Modern processors rely heavily on speculation to keep the pipeline filled and consequently execute and commit instructions as close to maximum capacity as possible.

• To improve instruction-level parallelism, the processor core needs to fetch and decode multiple instructions per cycle and has come to rely on incredibly accurate branch prediction.
• However, this comes at cost of the increased area and complexity which is needed for modern high accuracy branch predictors.
• The key idea described in this work is to use hyperdimensional computing and sparse distributed memory principles to create a novel branch predictor that can deliver complex predictions for a fraction of the current area.