Accelerating Deep Learning Inference on Mobile Systems
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International Conference on AI and Mobile Services Services Conference Federation (SCF) San Diego, CA, USA June 2019 Artificial Intelligence on the edge is a matter of great importance towards the enhancement of smart devices that rely on operations with real-time constraints. Despite the rapid growth of computational power in embedded systems, such as smartphones, wearable devices, drones and FPGAs, the deployment of highly complex and considerably big models remains challenging. Optimized execution requires managing memory allocation efficiently, to avoid overloading, and exploiting the available hardware resources for acceleration, which is not trivial given the non standardized access to such resources. We present PolimiDL, an open source framework for the acceleration of Deep Learning inference on mobile and embedded systems with limited resources and heterogeneous architectures. Experimental results show competitive results w.r.t. TensorFlow Lite for the execution of small models.
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Accelerating Deep Learning Inference on Mobile Systems
- 1. Accelerating Deep Learning Inference on Mobile Systems Darian Frajberg Carlo Bernaschina Christian Marone Piero Fraternali June 27, 2019
- 2. 2 Typical implementations of Deep Learning (DL) models focus on the maximization of accuracy for a given task. Architectures to achieve such an objective have become significantly deeper and more complex over time. Top-5 error (%) Introduction
- 3. 3 Artificial Intelligence (AI) on the edge is a matter of great importance towards the enhancement of smart devices that rely on operations with real-time constraints. Despite the rapid growth of computational power in embedded systems, such as smartphones, wearable devices, drones and FPGAs, the deployment of highly complex and considerably big DL models remains challenging. Introduction
- 4. 4 Introduction Cloud-offloading issues: • Cost • Availability • Coverage • Latency • Privacy
- 5. 5 Related work • Compression techniques. – Quantization – Pruning – Knowledge distillation – Tensor decomposition • Optimized model architectures. – SqueezeNet – MobileNet v1 – MobileNet v2 – MnasNet • Hardware acceleration. – Neural Networks API – OpenGL – Vulkan – Metal
- 6. 6 Related work • Heterogeneous computing scheduling. – Mobile GPU – Custom implementations with access to hardware primitives • Mobile Deep Learning frameworks. – TensorFlow Lite – Caffe2 – CoreML
- 7. 7 Limitations 1. Hardware Acceleration primitives are still not completely standardized and stable, but are tightly dependent on SoC vendors. 2. Retraining or modifying the architecture of ready- to-use models can be extremely time-consuming. 3. Post-training compression of already small models can detriment accuracy.
- 8. 8 Use case PeakLens is a real world mobile app that combines Augmented Reality and Computer Vision (CV) for the identification of mountain peaks. It processes sensor readings and camera frames in real-time by using an efficient on-board Deep Learning-powered CV module. +400k installs in Android
- 9. 9 Requirements 1. Focus on execution. It should be possible to train a model using tools already known to the developer. The framework should focus just on execution concerns, without the need of re-training. 2. Minimum dependencies. It should be possible to execute an optimized model independently of the Operating System, hardware platform or model storage format. 3. Easy embedding. It should be possible to embed the framework and optimized models into existing applications easily, without the need of ad-hoc integration procedures. 4. End-to-end optimization. Optimization should be applied as early as possible and span the model life-cycle (generation, compilation, initialization, configuration, execution). 5. Offline support. Computation should occur only on-board the embedded system, without the need of a network connection for work off-loading. 6. No accuracy loss. The acceleration for constrained devices should not reduce accuracy w.r.t. to the execution on a high performance infrastructure.
- 10. 10 The PolimiDL Framework PolimiDL is an open source framework for accelerating DL inference on mobile and embedded systems, which was started when no efficient off- the-shelf edge solutions were available. Implementation is generic and aims at supporting devices with limited power and heterogeneous architectures.
- 12. 12 The PolimiDL Framework • Generation-time optimizations. – Layers fusion. Consecutive in-place layers with identical filter size can be fused into one single layer, thus reducing the number of iterations over the cells of an input matrix. Examples: • Bias + ReLU = Bias_ReLU • Batch_Normalization + ReLu6 = BatchNormalization_ReLU6
- 13. 13 The PolimiDL Framework • Generation-time optimizations. – Weights fusion. Layers applying functions with constant terms comprising multiple weights can be pre-computed and encoded as unique constant weights, thus reducing operations at run-time and potential temporary memory allocation. Example: • Batch Normalization (BN)
- 14. 14 The PolimiDL Framework • Generation-time optimizations. – Weights rearrangement. Weights associated to predefined Convolutional layer types are stored in an order such that Eigen’s GEMM matrix operations do not require any memory reshaping at run-time.
- 16. 16 The PolimiDL Framework • Compile-time optimizations. – Fixed network architecture. The architecture of a model is fixed at compile-time, which enables the compiler to perform per-layer optimizations. .SO
- 17. Layer input Layer output Layer output Layer input Layer input Layer output 17 The PolimiDL Framework • Compile-time optimizations. – Shared memory allocation & “tick-tock” piping. The memory required by a model can be reduced and allocated efficiently by exploiting spatial locality and inverting the input and output buffers of subsequent layers. Temporary data
- 19. 19 The PolimiDL Framework • Initialization-time optimizations. – Memory pre-allocation. Memory requirements can be reduced by fusing the 3 buffers into a single one. During initialization, each layer is queried about its memory size requirements. Layer input Layer output Temporary data
- 20. 20 The PolimiDL Framework • Initialization-time optimizations. – Small tasks for low memory consumption. The operation of certain layers is divided into smaller tasks that can be executed independently, thus not performing a complete input unroll, but maintaining a fixed required size for the temporary memory. Task T0 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 T11 T12 T13 T14 T15 T16 T17 T18 T19 T20 T21 T22 T23 T24
- 22. 22 The PolimiDL Framework • Configuration-time optimizations. – Scheduling optimization. The optimal size for a scheduled task may vary depending on the specific layer, the underlying architecture, or even on the input size for Fully Convolutional Neural Networks. The size can be: • Set to a default value. • Inferred by executing a profiling routine. • Loaded from previous profiling routine executions.
- 24. 24 The PolimiDL Framework • Run-time optimizations. – Dynamic workload scheduling. Dynamic multithreaded scheduling of tasks can adapt well to different contexts such as ARM big.LITTLE architecture and allows cores to be better exploited.
- 25. 25 The PolimiDL Framework Layers coverage Layer name In place Temp. memory Schedulable Convolution X √ √ Depthwise convolution X √ √ Pointwise convolution (out_channels <= in_channels) √ √ √ Pointwise convolution (out_channels > in_channels) X X √ Max Pooling X √ X Average Pooling X √ √ Batch normalization √ X √ Bias √ X X ReLU/ReLU6 √ X X
- 26. 26 Evaluation Compare inference execution time of PolimiDL and TensorFlow Lite. Execute benchmark over: – Multiple models – Multiple devices with heterogeneous architectures
- 27. 27 Experimental setup Models Model Task Input size Paramete rs Mult-Adds PeakLens original Image Segmentation 320 x 240 x 3 429K 2G PeakLens optimized Image Segmentation 320 x 240 x 3 21K 198M MobileNet v1 Object Classification 224 x 224 x 3 4.24M 569M
- 28. 28 Experimental setup Device Android V. Chipset CPU RAM Asus ZenFone 2 5.0 Z2560 Intel Atom 2-cores 1.6 GHz (4 threads) 2 GB Google Pixel 9.0 MSM8996 Qualcomm Snapdragon 821 2-cores 2.15 Ghz Kryo + 2-cores 1.6 Ghz Kryo (4 threads) 4 GB LG G5 SE 7.0 MSM8976 Qualcomm Snapdragon 652 4-cores 1.8 GHz Cortex-A72 + 4-cores 1.2 GHz Cortex-A53 (8 threads) 3 GB LG Nexus 5X 8.1 MSM899 Qualcomm Snapdragon 808 4-cores 1.44 GHz Cortex-A53 + 2-cores 1.82 GHz Cortex-A57 (6 threads) 2 GB Motorola Nexus 6 7.0 Qualcomm Snapdragon 805 4-cores 2.7 GHz Krait (4 threads) 3 GB One Plus 6T 9.0 SDM845 Qualcomm 4-cores 2.8 GHz Kryo 385 + 4-cores 1.8 GHz Kryo 385 (8 threads) 6 GB Devices
- 29. 29 Experimental results Device TensorFlow Lite (ms) PolimiDL (ms) Asus Zenfone 2 1672.67 1138.00 (-31.96%) Google Pixel 255.33 171.00 (-33.03%) LG G5 SE 290.00 209.00 (-27.93%) LG Nexus 5X 370.33 342.33 (-7.56%) Motorola Nexus 6 505.33 215.67 (-57.32%) One Plus 6T 144.33 91.00 (-36.95%) Average (-32.46%) PeakLens original
- 30. 30 Experimental results Device TensorFlow Lite (ms) PolimiDL (ms) Asus Zenfone 2 807.67 179.33 (-77.80%) Google Pixel 95.00 35.33 (-62.81%) LG G5 SE 138.33 68.00 (-50.84%) LG Nexus 5X 193.00 80.33 (-58.38%) Motorola Nexus 6 225.67 66.00 (-70.75%) One Plus 6T 68.67 22.67 (-66.99%) Average (-64.59%) PeakLens optimized
- 31. 31 Experimental results Device TensorFlow Lite (ms) PolimiDL (ms) Asus Zenfone 2 775.33 377.33 (-51.33%) Google Pixel 82.33 82.67 (+0.40%) LG G5 SE 274.67 259.00 (-5.70%) LG Nexus 5X 225.00 234.33 (+4.15%) Motorola Nexus 6 298.33 176.00 (-41.01%) One Plus 6T 56.67 51.67 (-8.82%) Average (-17.05%) MobileNet v1
- 32. Concept – Open source framework for accelerating Deep Learning inference on mobile and embedded systems, which has proved competitive w.r.t. TensorFlow Lite. Future work – Extended support for more layers, quantization and conversion from more DL frameworks. – Extended evaluation with more configurations, metrics and devices. 32 Conclusions
- 33. 33 Thanks For Your Attention! Accelerating Deep Learning Inference on Mobile Systems Darian Frajberg Carlo Bernaschina Christian Marone Piero Fraternali https://github.com/darianfrajberg/polimidldarian.frajberg@polimi.it
Editor's Notes
- Compression techniques target large scale architectures and aim at reducing the number of parameters and floating point operations (FLOPs), possibly tolerating small accuracy drops in favor of execution acceleration and optimization of computational resources, storage, memory occupation and energy consumption. Lightweight architectures with compact layers pursue the design of an optimized network topology, yielding small, fast and accurate models, suitable for resource-constrained devices. HA is the use of dedicated hardware to complement general-purpose CPUs and perform computationally intensive work more efficiently, e.g. by favoring specific operations and data-parallel computation.
- Heterogeneous computing scheduling comprises the design of strategies to efficiently coordinate and distribute the workload among processors of different types. Frameworks for the execution of DL models on mobile and embedded systems pursue optimized deployment on devices with limited resources, by managing memory allocation efficiently and exploiting the available hardware resources at best.
- Optimized execution requires managing memory allocation efficiently, to avoid overloading, and exploiting the available hardware resources for acceleration, which is not trivial given the non standardized access to such resources.
- Evaluation exploits hardware with limited resources and models with a small-size architecture achieving a good trade-o between accuracy and latency. Three models with diverse characteristics, listed in Table 2, are evaluated.