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Deep neural network (DNN) inference is increasingly being executed on mobileand embedded platforms due to low latency and better privacy. However,efficient deployment on these platforms is challenging due to the intensivecomputation and memory access. We propose a holistic system design for DNNperformance and energy optimisation, combining the trade-off opportunities inboth algorithms and hardware. The system can be viewed as three abstractlayers: the device layer contains heterogeneous computing resources; theapplication layer has multiple concurrent workloads; and the runtime resourcemanagement layer monitors the dynamically changing algorithms' performancetargets as well as hardware resources and constraints, and tries to meet themby tuning the algorithm and hardware at the same time. Moreover, We illustratethe runtime approach through a dynamic version of 'once-for-all network'(namely Dynamic-OFA), which can scale the ConvNet architecture to fitheterogeneous computing resources efficiently and has good generalisation fordifferent model architectures such as Transformer. Compared to thestate-of-the-art Dynamic DNNs, our experimental results using ImageNet on aJetson Xavier NX show that the Dynamic-OFA is up to 3.5x (CPU), 2.4x (GPU)faster for similar ImageNet Top-1 accuracy, or 3.8% (CPU), 5.1% (GPU) higheraccuracy at similar latency. Furthermore, compared with Linux governor (e.g.performance, schedutil), our runtime approach reduces the energy consumption by16.5% at similar latency.

Dynamic DNNs Meet Runtime Resource Management on Mobile and Embedded Platforms