Resistive Bloom filters: From approximate membership to approximate computing with bounded errors

Approximate computing provides an opportunity for exploiting application characteristics to trade the accuracy for gains in energy efficiency. However, such opportunity must be able to bound the error that the system designer provides to the application developer. Space-efficient probabilistic data structure such as Bloom filter can provide one such means. Bloom filter supports approximate set membership queries with a tunable rate of false positives (i.e., errors) and no false negatives. We propose a resistive Bloom filter (ReBF) to approximate a function by tightly integrating it to a functional unit (FU) implementing the function. ReBF approximately mimics partial functionality of the FU by recalling its frequent input patterns for computational reuse. The accuracy of the target FU is guaranteed by bounding the ReBF error behavior at the design time. We further lower energy consumption of a FU by designing its ReBF using low-power memristor arrays. The experimental results show that function approximation using ReBF for five image processing kernels running on the AMD Southern Islands GPU yields on average 24.1% energy saving in 45 nm technology compared to the exact computation.