HTMMU:a memory management unit for irregular memory access in deep learning
The diversification and complexity of artificial intelligence applications lead to irregular memory access pat-tern.The irregular memory access pattern can be defined as bursty and sparse memory access requests,which brings great challenges to the deployment of intelligent applications on mobile devices with strictly limited memory resources.This irregular memory access pattern has caused the memory management unit(MMU)in existing archi-tectures to face the problems of low throughput and long latency,making it a bottleneck of the system.To solve this problem,this paper proposes a novel MMU architecture called high-throughput MMU(HTMMU).HTMMU uses multi-stream parallelism,enhances filtering of redundant requests and allocates limited on-chip memory more rea-sonably to improve system memory access efficiency.Experimental results show that when dealing with the irregular memory accesses in artificial intelligence algorithms,compared with the current MMU design,HTMMU achieves 2.43 times speedup averagely,and reduces the average latency by 65.9％with less than 3.0％area overhead.
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