Raspbian Desktop LoveRPi US Edition for Raspberry Pi Boards

Love Our Pi

The folks over at LoveRPi announced their customized Raspbian Desktop images for Raspberry Pi boards. This release is based on the 2018-11-13 release of Raspbian Desktop Full with our modifications to improve performance and stability.




Modifications:

  • Larger FAT32 partition starting at standard 1MB offset
  • BTRFS root filesystem with compression, checksumming, and metadata duplication
  • Automatic kernel initrd generation and pruning on upgrade (apt-get and rpi-update)
  • Automatic filesystem resize to disk size without reboot
  • Automatic swap partition generation on first start
  • Disable overscan by default
  • Rotate LCD for correct orientation on Raspberry Pi Touchscreen Display
  • Change radio frequency support, locale, and timezone to US
  • Include touchscreen keyboard and vim by default
  • Remove unnecessary piwiz autostart
  • Reduced image size by 1GB for faster flashing
  • Desktop link assets for quick reference (removable)

Supported Boards:

  • Raspberry Pi 3 Model B+
  • Raspberry Pi 3 Model B
  • Raspberry Pi 2 Model B
  • Raspberry Pi Model B
  • Raspberry Pi Zero W
  • Raspberry Pi Zero

DOWNLOAD LINK: http://share.loverpi.com/board/raspberry-pi/raspbian/2018-11-13-raspbian-stretch-desktop-loverpi.zip
FILENAME: 2018-11-13-raspbian-stretch-desktop-loverpi.zip
SHA512SUM: 7dcd6402e52fd3981d150185368bde018c46963d629f93fe6059b71ef033066a5ddcc95589e32033769722c40df7dcd0b9456c54d8704949698c5603c5b211c5

Lite image available here.

Jetson Nano Brings AI Computing to Everyone

NVIDIA announced the Jetson Nano Developer Kit at the 2019 NVIDIA GPU Technology Conference (GTC), a $99 computer available now for embedded designers, researchers, and DIY makers, delivering the power of modern AI in a compact, easy-to-use platform with full software programmability. Jetson Nano delivers 472 GFLOPS of computing performance with a quad-core 64-bit ARM CPU and a 128-core integrated NVIDIA GPU. It also includes 4GB LPDDR4 memory in an efficient, low-power package with 5W/10W power modes and 5V DC input, as shown below.


Jetson Nano Developer Kit (80x100mm), available now for $99

The Jetson Nano Developer Kit fits in a footprint of just 80x100mm and features four high-speed USB 3.0 ports, MIPI CSI-2 camera connector, HDMI 2.0 and DisplayPort 1.3, Gigabit Ethernet, M.2 Key-E module, MicroSD card slot, and 40-pin GPIO header. The ports and GPIO header works out-of-the-box with a variety of popular peripherals, sensors, and ready-to-use projects, such as the 3D-printable deep learning JetBot that NVIDIA has open-sourced on GitHub.

The devkit boots from a removable MicroSD card which can be formatted and imaged from any PC with an SD card adapter. The devkit can be conveniently powered via either the Micro USB port or a 5V DC barrel jack adapter. The camera connector is compatible with affordable MIPI CSI sensors including modules based on the 8MP IMX219, available from Jetson ecosystem partners. Also supported is the Raspberry Pi Camera Module v2, which includes driver support in JetPack. Table 1 shows key specifications.

Jetson Nano specifications

The devkit is built around a 260-pin SODIMM-style System-on-Module (SoM), shown in figure 2. The SoM contains the processor, memory, and power management circuitry. The Jetson Nano compute module is 45x70mm and will be shipping starting in June 2019 for $129 (in 1000-unit volume) for embedded designers to integrate into production systems. The production compute module will include 16GB eMMC onboard storage and enhanced I/O with PCIe Gen2 x4/x2/x1, MIPI DSI, additional GPIO, and 12 lanes of MIPI CSI-2 for connecting up to three x4 cameras or up to four cameras in x4/x2 configurations. Jetson’s unified memory subsystem, which is shared between CPU, GPU, and multimedia engines, provides streamlined ZeroCopy sensor ingest and efficient processing pipelines.

Deep Learning Inference Benchmarks

Jetson Nano can run a wide variety of advanced networks, including the full native versions of popular ML frameworks like TensorFlow, PyTorch, Caffe/Caffe2, Keras, MXNet, and others. These networks can be used to build autonomous machines and complex AI systems by implementing robust capabilities such as image recognition, object detection and localization, pose estimation, semantic segmentation, video enhancement, and intelligent analytics.

Figure 3 shows results from inference benchmarks across popular models available online. The inferencing used batch size 1 and FP16 precision, employing NVIDIA’s TensorRT accelerator library included with JetPack 4.2. Jetson Nano attains real-time performance in many scenarios and is capable of processing multiple high-definition video streams.

Multi-Stream Video Analytics

Jetson Nano processes up to eight HD full-motion video streams in real-time and can be deployed as a low-power edge intelligent video analytics platform for Network Video Recorders (NVR), smart cameras, and IoT gateways. NVIDIA’s DeepStream SDK optimizes the end-to-end inferencing pipeline with ZeroCopy and TensorRT to achieve ultimate performance at the edge and for on-premises servers. The video below shows Jetson Nano performing object detection on eight 1080p30 streams simultaneously with a ResNet-based model running at full resolution and a throughput of 500 megapixels per second (MP/s).