It looks attractive to coordinate racetrack-memory(RM) and stochastic-computing (SC) jointly to build an ultra-low power neuron-architecture. However, the above combination has always been questioned in a fatal weakness that the narrow bit-view of the RM-MTJ structure, a.k.a. shift-and-access pattern, cannot physically match the great throughput of direct-stored stochastic sequences. Fortunately, a recently developed Transverse-Read(TR) provides a wider segment-view to RM via detecting the resistance of domain-walls between a couple of MTJs on single nanowire, therefore RM can be enhanced with a faster access to the sequences without any substantial domain-shift. To utilize TR for a power-efficient SC-DNNs, we propose a segment-based compression to leverage one-cycle TR to only read those kernel segments of stochastic sequences, meanwhile, remove a large number of redundant segments for ultra-high storage density. In decompression stage, low-discrepancy stochastic sequences can be quickly reassembled by a select-and-output loop using kernel segments rather than slowly regenerated by costly SNGs. Since TR can provide an ideal in-memory acceleration in one-counting, counter-free SC-MACs are designed and deployed near RMs to form a power-efficient neuron-architecture, in which, the binary results of TR are activated straightforward without sluggish APCs. The results show that under the TR aided RM model, the power efficiency, speed, and stochastic accuracy of Seed-based Fast Stochastic Computing significantly enhance the performance of DNNs. The speed of computation is 2.88x faster in Lenet-5 and 4.40x faster in VGG-19 compared to the CORUSCANT. The integration of TR with RTM is deployed near the memory to create a power-efficient neuron architecture, eliminating the need for slow Accumulative Parallel Counters (APCs) and improving access speed to stochastic sequences.

翻译：暂无翻译