HiPEAC Computing Systems Week 2022_Mario Porrmann presentation
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Performance Evaluation and Benchmarking in VEDLIoT. Mario Porrmann. HiPEAC Computing Systems Week, Tampere, Finland, April 2022
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HiPEAC Computing Systems Week 2022_Mario Porrmann presentation
- 1. Mario Porrmann Osnabrück University 27. April 2022 Performance Evaluation and Benchmarking – Reconfigurable Accelerators in VEDLIoT
- 2. 2 Applications Requirements Security & Safety Hardware Plattforms Microservers & Accelerators Middleware Embedded/ Far Edge Near Edge Cloud Safety & Robustness Modelling & Verification Jetson AGX NVIDIA Xavier COM-HPC Xilinx Zynq UltraScale+ SMARC Xilinx Zynq UltraScale+ Coral SoM Xilinx Kria RPi CM4 ARVSOM Smart Home Industrial IoT Automotive AI Open Call Monitoring Trusted Execution & Communication RISC-V extensions Optimizer Emulation Benchmarking & Deployment uRECS t.RECS RECS|Box Big Picture
- 3. 3 Applications Requirements Security & Safety Hardware Plattforms Microservers & Accelerators Middleware Embedded/ Far Edge Near Edge Cloud Safety & Robustness Modelling & Verification Jetson AGX NVIDIA Xavier COM-HPC Xilinx Zynq UltraScale+ SMARC Xilinx Zynq UltraScale+ Coral SoM Xilinx Kria RPi CM4 ARVSOM Smart Home Industrial IoT Automotive AI Open Call Monitoring Trusted Execution & Communication RISC-V extensions Optimizer Emulation Benchmarking & Deployment uRECS t.RECS RECS|Box Big Picture Hardware Plattforms Microservers & Accelerators Embedded/ Far Edge Near Edge Cloud Jetson AGX NVIDIA Xavier COM-HPC Xilinx Zynq UltraScale+ SMARC Xilinx Zynq UltraScale+ Coral SoM Xilinx Kria RPi CM4 ARVSOM uRECS t.RECS RECS|Box • FPGA-based Accelerators in VEDLIoT • Dynamic Reconfiguration of Accelerators • First Results on Performance and Energy Efficiency • Workflow for Configurable Soft SoCs
- 4. 4 FPGA Infrastructure • FPGA base architecture • Integration of the required Interfaces and accelerators • Support for dynamic run-time reconfiguration • Exchange accelerators on the FPGA at run-time to increase resource efficiency and flexibility • FPGA task deployment mechanism • Migration of a task from one FPGA to another FPGA Logic Cells 85k 2800k 25.2M 75.6M
- 5. 5 Basic FPGA Infrastructure • FPGA base architecture for the µ.RECS • Block-based design enabling easy customization of the FPGA platform in the µ.RECS • Front-end based on Xilinx Vitis with additional (optional) IP-cores from LiteX • Scripting approach for complete system design • Easy porting to new FPGAs and FPGA platforms, esp. µ.RECS. t.RECS, RECS|Box • Flexible integration of accelerators • Integration of the required Interfaces for communication (Ethernet, PCIe, etc) as well as sensors and actuators targeted in the use cases • PetaLinux enables easy access to the system and to integrated accelerators for software developers • µ.RECS testbed for early evaluation SMARC Module SoC FPGA-Fabric Processing System HDMI CSI PCIe x4 GigE USB DDR (PS) Memory Subsystem Interrupt Controller Dual/Quad Arm Cortex- A53 Dual Arm Cortex-R5 I/O Interfaces AXI Accelerator(s) AXI AXI-Lite AXI-Lite GPIO, UART DDR (PL) Xilinx/ LiteX Memory Ctrl eMMC Flash SD GPIO, UART I/O Ctrl SATA Clk Platform Mgmt, System Funct. & Configuration HDMI CSI
- 6. 6 FPGA Base Architecture for µ.RECS SMARC Module SoC FPGA-Fabric Processing System HDMI CSI PCIe x4 GigE USB DDR (PS) Memory Subsystem Interrupt Controller Dual/Quad Arm Cortex- A53 Dual Arm Cortex-R5 I/O Interfaces AXI Accelerator(s) AXI AXI-Lite AXI-Lite GPIO, UART DDR (PL) Xilinx/ LiteX Memory Ctrl eMMC Flash SD GPIO, UART I/O Ctrl SATA Clk Platform Mgmt, System Funct. & Configuration HDMI CSI
- 7. 7 First Reference Design Based on Xilinx DPU • Baseline for evaluation of FPGA accelerators developed in VEDLIoT • Xilinx Deep Learning Processor Unit (DPU) • Programmable engine for convolutional neural networks • Easy integration as an IP core in Xilinx UltraScale+ MPSoCs • Configurable hardware architecture (e.g., parallelism, memory/DSP usage) • Evaluation on various platforms with Xilinx UltraScale+ MPSoCs • ZU3EG on Avnet Ultra96-v2 (154k Logic Cells) • ZU4EG in the µ.RECS testbed (192k Logic Cells) • ZU15EG on Trenz TE0808 MPSoC Module (747k Logic Cells) • ZU19EG on Trenz COM-HPC Module in t.RECS (1,143k Logic Cells) DPU Peak ops/clock Peak performance (300 MHz) [GOPS] Peak performance (200 MHz) [GOPS] B512 512 153.6 102.4 B2304 2304 691.2 460.8 B4096 4096 1228.8 819.2
- 8. 8 First Reference Design Based on Xilinx DPU • Example implementation utilizing the µ.RECS testbed • SMARC module SECO RUSSELL • Xilinx Zynq UltraScale+ XCZU4EG-1 FPGA • Quad-core Arm Cortex-A53, Dual-core Arm Cortex-R5 • 88k 6-input look-up tables (LUTs) • 176k Flip-Flops (FFs) • 728 DSP Slices • 128 36kb BRAM blocks (4.5 Mb total) • 48 288kb URAM blocks (13.5 Mb total) • 2 GByte 64-Bit DDR4 SDRAM (PS) • 512 MByte 64-Bit DDR4 SDRAM (PL) DPU Configuration Resources B512 B2304 B4096 Complete Design LUTs 34,456 39.2% 47,107 53.6% 56,685 64.5% FFs 43,557 24.8% 78,215 44.5% 107,732 61.3% DSPs 110 15.1% 422 58.0% 690 94.8% BRAMs 13.5 10.5% 61 47.7% 81 63.3% URAMs 16 33.3% 40 83.3% 48 100% Base Design LUTs 8,439 9.6% 8,434 9.6% 8,456 9.6% FFs 10,205 5.8% 10,205 5.8% 10,205 5.8% DSPs 0 0% 0 0% 0 0% BRAMs 4 3.1% 4 3.1% 4 3.1% URAMs 0 0% 0 0% 0 0% DPU LUTs 26,017 29.6% 38,673 44.0% 48,229 54.9% FFs 33,352 19.0% 68,010 38.7% 97,527 55.5% DSPs 110 15.1% 422 58.0% 690 94.8% BRAMs 9.5 7.4% 57 44.5% 77 60.2% URAMs 16 33.3% 40 83.3% 48 100%
- 9. 9 Efficient Utilization of the Xilinx DPU • Multithreading is crucial for high performance • Environment supporting semi-automatic realization and evaluation of multithreading during application development • Execution Time – Single-threaded • Execution Time – Multi-threaded Read Data Preproc. DPU Processing Post. Read Data Preproc. Post. DPU Processing DPU Processing Post. Read Data Preproc. Read Data Preproc. t1 t2 t3 t4 t0 t0 ttotal
- 10. 10 Efficient Utilization of the Xilinx DPU • Performance and power monitoring for single- and multi-threaded implementations • Detailed power measurements on RECS platforms • Power-aware profiling and optimization
- 11. 11 Example DSE Using Different DPU Configurations
- 12. 12 Example DSE Using Different DPU Configurations
- 13. 13 Example DSE Using Different DPU Configurations
- 14. 14 Benchmark Performance of DL Accelerators YoloV4 [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLR… [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRANGE] [CELLRA… [CELLRANGE] 10 100 1000 10000 2 4 8 16 32 64 128 Performance [GOPS] Power [Watt] INT8 FP16 FP32 ZU3 ZU15
- 15. 15 Dynamic Reconfiguration of DL Accelerators • Change the characteristics of the DL accelerator at run-time (e.g., change performance-power trade-off or performance-accuracy trade-off) SMARC Module SoC FPGA-Fabric Processing System HDMI CSI PCIe x4 GigE USB DDR (PS) Memory Subsystem Interrupt Controller Dual/Quad Arm Cortex- A53 Dual Arm Cortex-R5 I/O Interfaces AXI AXI-Lite GPIO, UART DDR (PL) Xilinx/ LiteX Memory Ctrl eMMC Flash SD GPIO, UART I/O Ctrl SATA Platform Mgmt, System Funct. & Configuration HDMI CSI Clk AXI CB AXI –Lite CB Disconnect PR-Region DFX Accelerator A Accelerator B
- 16. 16 Dynamic Reconfiguration of DL Accelerators SMARC Module SoC FPGA-Fabric Processing System HDMI CSI PCIe x4 GigE USB DDR (PS) Memory Subsystem Interrupt Controller Dual/Quad Arm Cortex- A53 Dual Arm Cortex-R5 I/O Interfaces AXI AXI-Lite GPIO, UART DDR (PL) Xilinx/ LiteX Memory Ctrl eMMC Flash SD GPIO, UART I/O Ctrl SATA Platform Mgmt, System Funct. & Configuration HDMI CSI Clk AXI CB AXI –Lite CB Disconnect Accelerator Disconnect Accelerator Accelerator PR-Region PR-Region DFX • Change the characteristics of the DL accelerator at run-time (e.g., change performance-power trade-off or performance-accuracy trade-off)
- 17. 17 Reconfigurable DL Accelerators • Accelerator to be used for the codesign approach: Generation of dataflow-architectures based on C++ templates • Support for inference and training • Targeting CNNs, deep reinforcement learning, and federated learning • Definition of parameterizable layer templates in C++ (e.g., convolution, fully connected, pooling, and activation functions, …) • Parameterizable, e.g., quantization (from low bit-width INT to float) • Optimized for high-level synthesis • All layers integrate three functions (if required): inference/forward propagation, backpropagation, and update function • Inference utilizes only forward path • Learning (DeepRL): utilizes the full functionality of the layer templates
- 18. 18 Soft SoC Platform • Generation of soft SoC platforms • Utilize RISC-V soft cores • Generic interface to AI-Accelerators • Modelled in an open source emulation environment • Utilize LiteX SoC generator • Run-time reconfiguration • Accelerators • Processor cores FPGA Base Architecture AI-Accelerator Run-Time Reconfiguration Interface
- 19. 19 • Configurable soft SoC generator provides a platform for low power AI accelerator exploration • The generator enables a functionality to generate a system with a set of peripherals required for a specific tasks • Scalable from MCU-class to Linux-capable platforms • Support for generic, vendor independent accelerator integration interface makes it a perfect AI research platform • Portable across different hardware, based on open-source tooling • CFUs - Custom Function Units – custom accelerators designed for specific workflows, tightly coupled with the CPU • Accessed via custom RISC-V instructions • Can be implemented in high-level hardware description languages, like, e.g., Python-based Amaranth Configurable SoC for ML Workflows
- 20. 20 • CFUs offer great flexibility • Test various dedicated accelerators for specific workflows • Renode simulation framework extended with CFU support • Co-simulating functional models of the SoC with verilated, cycle-accurate CFUs • Invaluable tool for development • Massive continuous integration testing • Different CFU implementations • Different inputs • Allows for automatic result comparison and analysis • Everything open-sourced Configurable SoC for ML Workflows
- 21. 21 • Platform • Hardware: Scalable, heterogeneous, distributed • Accelerators: Efficiency boost by FPGA and ASIC technology • Toolchain: Optimizing Deep Learning for IoT • Use cases • Industrial IoT • Automotive • Smart Home • Open call • Open for submissions until 8. May • Early use and evaluation of VEDLIoT technology Very Efficient Deep Learning for IoT – VEDLIoT
- 22. 22 Follow our work ⇒ https://twitter.com/VEDLIoT ⇒ https://www.linkedin.com/company/vedliot/ ⇒ https://vedliot.eu Be part of it ⇒ Open call NOW! ⇒ Allows early use and evaluation of VEDLIoT technology
- 23. 23 Thank you for your attention.
- 24. 24 DL Accelerator CPU GPU TPU Compiler DL model Heterogenenous DL Accelerator DL Accelerator FPGA Compiler HW Spec DL model Reconfigurable DL Accelerator DL Accelerator FPGA Compiler DL model HW Spec HW Spec Compiler Dynamically Reconfigurable DL Accelerator DL Accelerator FPGA Compiler Co- Design DL model Co-Designed DL Accelerator Deep Learning Accelerators
- 25. 25 Dynamic Reconfiguration of DL Accelerators • Utilize dynamic reconfiguration • Change the complete DL model and the corresponding accelerator at run-time depending on application requirements • Change the characteristics of the DL accelerator at run-time (e.g., change performance-power trade-off or performance-accuracy trade-off) • Enable accelerator to be partially reconfigured for different phases of the application
- 26. 26 First Reference Design Based on Xilinx DPU Example • Performance and power evaluation for YoloV4 • Trade-off latency vs. performance Platform SM-B71 on SOM-DB2500 Carrier DPU B3136 x1, 300MHz B4096 x1, 300MHz Number of threads 1 2 4 1 2 4 Latency [ms] 120.34 198.62 383.72 93.42 144.51 276.33 Achieved performance [Inferences/s] 8.28 10.76 10.76 10.66 15.12 15.12 Achieved performance [GOPS] 500.11 649.90 649.90 643.86 913.25 913.25 Peak performance [GOPS] 940.8 940.8 940.8 1228.8 1228.8 1228.8 Performance Ratio 53.16% 69.08% 69.08% 52.40% 74.32% 74.32% Cost Metrics Power [W] 11.20 12.49 12.51 13.14 15.42 15.44 Idle Power [W] 0.07/7.09 0.07/7.09 0.07/7.09 0.07/7.56 0.07/7.56 0.07/7.56 Energy/Inference [J] 1.352 1.161 1.173 1.233 1.020 1.021 Power Efficiency [GOPS/W] 44.65 52.03 51.95 49.00 59.23 59.15