Hardware Acceleration of Feature Detection and Description Algorithms on Low Power Embedded Platforms
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1 Hardware Acceleration of Feature Detection and Description Algorithms on LowPower Embedded Platforms Onur Ulusel, Christopher Picardo, Christopher Harris, Sherief Reda, R. Iris Bahar, School of Engineering, Brown University
2 Image Processing in Mobile Systems Image processing is everywhere! Input data has changed from words/numbers to images Sensors have improved dramatically Image processing is a major driving factor in technological advancement Autonomization relies on image processing Mobile/Embedded platforms?? Realtime computing limited data bandwidth prefer local computing to offloading to cloud BUT image processing can be very computationally intensive and power hungry 2
3 Accelerating Image Processing on Low Power Embedded Platforms Meeting real time image processing requirements for many of these applications requires HW assisted acceleration Which algorithms do we accelerate? Feature detection and feature description are key building blocks for image retrieval, biometric identification, visual odometry, etc. Computational efficient detection and analysis of image features is critical for performance and energyefficiency
4 Hardware Acceleration for Energy Constrained Image Processing Low power embedded platforms Field Programmable Gate Arrays (FPGAs) Graphical Processing Units (GPUs) Low power general processors (CPUs) Xilinx Virtex 6 FPGAs Xilinx Zynq core NVIDIA GeForce GTX 680 GPUs 192 core NVIDIA Jetson TK1 Power 15 W <5W 195 W <12W 4
5 Our Contributions Comparative study of feature detection and description algorithms What are their computation kernel characteristics? Comparative study of platforms for embedded applications Advantages/disadvantages of each platform? Accelerating algorithms on different platforms How can algorithms be modified to better exploit available hardware of each platform? How does performance compare in terms of run time and energy consumption? 5
6 What is a feature? Feature Detection An interesting part of an image that can be used to identify objects Examples: Edges, corners, ridges, blobs flat region: no change within block edge: no change along the edge corner: change in all directions Slide adapted from Darya Frolova, Denis Simakov, Weizmann Institude 6
7 Feature Description Given the features, uniquely describe them so they can be matched in other images Descriptors summarize characteristics of the features E.g., intensity, orientation Descriptors should be distinctive and insensitive to local image deformations. Images from: R. Szeliski, Computer Vision: Algorithms and Applications 7
8 Accuracy and Runtime Comparisons Accuracy Percetage SIFT SURF BRIEF BRISK HoG (Histogram of Gradient) based Descriptors SIFT: ScaleInvariant Feature Transform SURF: Speeded Up Robust Features Binary Feature Descriptors BRIEF: Binary Robust Independent Elementary Features BRISK: Binary Robust Invariant Scalable Keypoints 10 CPU runtime (ms) log scale Precision (% of detected features that are correct) Recall (% of features detected) Runtime 8
9 FAST: Features from Accelerated Segment Test 12pixel continuity? If so then feature Rosten and Drummond, ECCV 06 Precompare pixels 1, 5, 9, and 13 to determine possibility for continuity On average 98.5% of the comparisons fail the continuity test at the precompare stage sliding window Bresenham Circle 9
10 BRIEF: Binary Robust Independent Elementary Features Compare intensities of pairs of points using Hamming distance BRIEF Sampling pattern 512 sampling pairs For each pair, X i is at (0,0) and Y i takes all possible values from coarse polar grid Chosen sampling pattern Sampling pairs are generated from a results in a 512bit region around center pixel characterization array 10
11 BRISK: Binary Robust Invariant Scalable Keypoints BRISK uses custom sampling pattern 512 sampling pairs generated from a region (like BRIEF) Distinguishes between short/long pairs Short pairs used similar to BRIEF to generate descriptor vectors based on intensity comparisons Long pairs used for orientation computation by rotating sampling pattern BRISK sampling pattern Red circles represent standard deviation of Gaussian smoothing 11
12 Start Read Input Frame Algorithm Flowchart FAST BRIEF For each pixel, p, apply the 7x7 filter of Bresenham circle p is a corner Generate N sampling pairs, X i and Y i, around p X i > Y i Feature Detection False D i = 0 False FAST feature detection BRIEF feature description Obtaining sampling window for feature description requires irregular access pattern D i = 1 Brief Feature Description Stop
13 Start Read Input Frame Algorithm Flowchart FAST BRISK For each pixel, p, apply the 7x7 filter of Bresenham circle p is a corner Generate N sampling pairs, X i and Y i, around p X i > Y i Feature Detection False False D i = 0 FAST feature detection BRISK feature description BRISK requires an extra step for orientation compensation A significant amount of extra hardware resources for this step D i = 1 Perform Orientation Compensation for BRISK Stop Feature Description
14 Experimental Embedded Platforms FPGA: MicroZED development board: 28nm Zynq 7020 SoC Artix7 FPGA 1GB DDR3 dualcore Arm Cortex A9 CPU (for debug and init. only) GPU & CPU: Jetson TK1 development kit 28nm Tegra K1 SoC Kepler GPU with 192 CUDA 950MHz Quadcore ARM Cortex A15 2.5GHz (single core activated) 2GB Memory Running OpenCV versions of FAST, BRIEF, BRISK 14
15 Feature Detection & Description: Block Diagram DDR3 ARM Cortex A9 CPU Central Interconnect Memory Interface Processing System (PS) 32b GP AXI Master Port AXI Interconnect Control 10Line word Buffer Enable & Sync signals Image data Buffer Address Generator and Register Array ZigZag Traversing Line Buffers X x x x Precompute units Equality Mask Size Register Array X 12 Circle Comparator Programmable Logic (PL) is_corner Smoothing & Region Generation Nwide comparator Orientation Compensation x&y coordinates descriptor orientation 15
16 Feature Detection & Description: Block Diagram DDR3 ARM Cortex A9 CPU Central Interconnect Memory Interface Processing System (PS) 32b GP AXI Master Port AXI Interconnect Control 10Line word Buffer Enable & Sync signals Image data Buffer Address Generator and Register Array ZigZag Traversing Line Buffers X x x x Precompute units Equality Mask Size Register Array X 12 Circle Comparator Programmable Logic (PL) is_corner Smoothing & Region Generation Nwide comparator Orientation Compensation x&y coordinates descriptor orientation FAST Feature Detection 16
17 Feature Detection & Description: Block Diagram BRIEF Descriptor DDR3 ARM Cortex A9 CPU Central Interconnect Memory Interface Processing System (PS) 32b GP AXI Master Port AXI Interconnect Control 10Line word Buffer Enable & Sync signals Image data Buffer Address Generator and Register Array ZigZag Traversing Line Buffers X x x x Precompute units Equality Mask Size Register Array X 12 Circle Comparator Programmable Logic (PL) is_corner Smoothing & Region Generation Nwide comparator Orientation Compensation x&y coordinates descriptor orientation 17
18 Feature Detection & Description: Block Diagram BRISK Descriptor DDR3 ARM Cortex A9 CPU Central Interconnect Memory Interface Processing System (PS) 32b GP AXI Master Port AXI Interconnect Control 10Line word Buffer Enable & Sync signals Image data Buffer Address Generator and Register Array ZigZag Traversing Line Buffers X x x x Precompute units Equality Mask Size Register Array X 12 Circle Comparator Programmable Logic (PL) is_corner Smoothing & Region Generation Nwide comparator Orientation Compensation x&y coordinates descriptor orientation 18
19 Feature Detection & Description: Block Diagram DDR3 ARM Cortex A9 CPU Central Interconnect Memory Interface Processing System (PS) 32b GP AXI Master Port AXI Interconnect Control 10Line word Buffer Enable & Sync signals Image data Data control logic for detection and description Buffer Address Generator and Register Array ZigZag Traversing Line Buffers X x x x Precompute units Equality Mask Size Register Array X 12 Circle Comparator Programmable Logic (PL) is_corner Descriptor logic Smoothing & Region Generation Nwide comparator Orientation Compensation x&y coordinates descriptor orientation Feature detection logic 19
20 Results: Runtime Runtime (ms) Intel i7 CPU ARM on Jetson Tegra GPU on Jetson Zynq FPGA FAST FASTBRIEF FASTBRISK 20
21 Results: Power & Energy 25 (detection) (detection description) Intel i7 CPU Embedded CPU Tegra GPU Zynq FPGA 20 Power (W) FAST FASTBRIEF FASTBRISK Energy (mj) Intel i7 CPU Embedded CPU Tegra GPU Zynq FPGA FAST FASTBRIEF FASTBRISK
22 Results: Profiling Not Selected Stall Reasons during GPU Computation 100% 80% Instruction Distribution Memory Throttle 60% Pipe Busy 40% 20% Execution Dependency 0% FPGA: FAST FPGA: FAST BRIEF GPU: FAST GPU: FAST BRIEF 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% 30.0% 35.0% 40.0% 45.0% Description Detection (BRIEF) (FAST) Feature description stalled due to memory throttle Needs better data management Load/Store FP/Integer Feature description for GPU implementation has bump in load/store ops Almost 10X more than just FAST 22
23 Results: FPGA Resource Utilization Resource utilization Lookup Tables Flip Flops Block RAMs FAST FAST BRIEF FAST BRISK % 32% 57% Distribution of resources 19% 14% 66% 37% 37% 27% LookUp Tables Flip Flops Block Rams FAST FASTBRIEF FASTBRISK BRISK requires significant amount of extra resources for smoothing and orienting Extra resources do not translate to much extra power 23
24 Conclusions FPGA outperforms CPUs and GPUs in terms of power & performance FAST BRISK: 36 fps vs. 147 fps FPGA amenable to various HW optimizations: deep pipelining, optimized memory access, precomputation FGPA implementations better for handling multiple kernels For GPUs, multiple kernels highly bounded by kernel scheduler and memory bottlenecks FPGA customization on layers better for tackling operations on multiple kernels. Use profiling on GPU implementation as first step to FPGA optimization identify nature of bottlenecks Customized FPGA HW can often better manage certain types of bottlenecks 24
Hardware Acceleration of Feature Detection and Description Algorithms on Low-Power Embedded Platforms
Hardware Acceleration of Feature Detection and Description Algorithms on Low-Power Embedded Platforms Onur Ulusel, Christopher Picardo, Christopher B. Harris, Sherief Reda and R. Iris Bahar School of Engineering
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