About Dustin Franklin

Dustin is a GPGPU developer and system architect for GE Intelligent Platforms. With a background in robotics and computational imaging, he works with integrators to deploy CUDA-accelerated embedded systems. Visit http://www.ge-ip.com/gpgpu for more info.
Figure 4: MMTI and trainable HoG pedestrian/vehicle detectors extract dynamic obstacles from HD video at runtime

Low-Power Sensing and Autonomy With NVIDIA Jetson TK1

Figure 1: simple TK1 block diagram
Figure 1: simple TK1 block diagram

NVIDIA’s Tegra K1 (TK1) is the first ARM system-on-chip (SoC) with integrated CUDA.  With 192 Kepler GPU cores and four ARM Cortex-A15 cores delivering a total of 327 GFLOPS of compute performance, TK1 has the capacity to process lots of data with CUDA while typically drawing less than 6W of power (including the SoC and DRAM).  This brings game-changing performance to low-SWaP (Size, Weight and Power) and small form factor (SFF) applications in the sub-10W domain, all the while supporting a developer-friendly Ubuntu Linux software environment delivering an experience more like that of a desktop rather than an embedded SoC.

Tegra K1 is plug-and-play and can stream high-bandwidth peripherals, sensors, and network interfaces via built-in USB 3.0 and PCIe gen2 x4/x1 ports.  TK1 is geared for sensor processing and offers additional hardware-accelerated functionality asynchronous to CUDA, like H.264 encoding and decoding engines and dual MIPI CSI-2 camera interfaces and image service processors (ISP).  There are many exciting embedded applications for TK1 which leverage its natural ability as a media processor and low-power platform for quickly integrating devices and sensors.

As GPU acceleration is particularly well-suited for data-parallel tasks like imaging, signal processing, autonomy and machine learning, Tegra K1 extends these capabilities into the sub-10W domain.  Code portability is now maintained from NVIDIA’s high-end Tesla HPC accelerators and the GeForce and Quadro discrete GPUs, all the way down through the low-power TK1.   A full build of the CUDA 6 toolkit is available for TK1, including samples, math libraries such as cuFFT, cuBLAS, and NPP, and NVIDIA’s NVCC compiler.  Developers can compile CUDA code natively on TK1 or cross-compile from a Linux development machine.  Availability of the CUDA libraries and development tools ensures seamless and effortless scalability between deploying CUDA applications on discrete GPUs and on Tegra.  There’s also OpenCV4Tegra available as well as NVIDIA’s VisionWorks toolkit.  Additionally the Ubuntu 14.04 repository is rich in pre-built packages for the ARM architecture, minimizing time spent tracking down and building dependencies.  In many instances applications can be simply recompiled for ARM with little modification, as long as source is available and doesn’t explicitly call out x86-specific instructions like SSE, AVX, or x86-ASM. NEON is ARM’s version of SIMD extensions for Cortex-A series CPUs.
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