Reducing the Power and Complexity of Path-Based Neural Branch Prediction

A conventional path-based neural predictor (PBNP) achieves very high prediction accuracy, but its very deeply pipelined implementation makes it both a complex and power-intensive component. One of the major reasons for the large complexity and power is that for a history length of h, the PBNP must use h separately indexed SRAM arrays (or suffer from a very long update latency) organized in an h-stage predictor pipeline. Each pipeline stage requires a separate row-decoder for the corresponding SRAM array, inter-stage latches, control logic, and checkpointing support. All of these add power and complexity to the predictor. We propose two techniques to address this problem. The first is modulo path-history which decouples the branch outcome history length from the path history length allowing for a shorter path history (and therefore fewer predictor pipeline stages) while simultaneously making use of a traditional long branch outcome history. The pipeline length reduction results in decreased power and implementation complexity. The second technique is bias-based filtering (BBF) which takes advantage of the fact that neural predictors already have a way to track strongly biased branches. BBF uses the bias weights to filter out mostly always taken or mostly always not-taken branches and avoids consuming update power for such branches. Our proposal is complexity effective because it decreases the power and complexity of the PBNP without negatively impacting performance. The combination of modulo path-history and BBF results in a slight improvement in predictor accuracy of 1% for 32KB and 64KB predictors, but more importantly the techniques reduce power and complexity by reducing the number of SRAM arrays from 30+ down to only 4-6 tables, and reducing predictor update activity by 4-5%.