FOSSASIA 2019 Schedule – March 14-17

FOSSASIA 2019 Schedule

As its name implies, FOSSASIA is a Free and Open Source Software  event taking place every year in Asia, more specifically in Singapore. I first discovered it last year, and published a virtual FOSSASIA 2018 schedule last year to give an idea about the subjects discussed at the event. It turns out FOSSASIA 2019 is coming really soon, as in tomorrow, so I’m a bit late, but I’ll still had a look at the schedule and made my own for the 4-day event.  Thursday – March 14, 2019 10:05 – 10:25 – For Your Eyes Only: Betrusted & the Case for Trusted I/O by Bunnie Huang, CTO Chibitronics Security vulnerabilities are almost a fact of life. This is why system vendors are increasingly relying on physically separate chips to handle sensitive data. Unfortunately, private keys are not the same as your private matters. Exploits on your local device still have the potential to grant bad actors access to your screen …

$15 Sparkfun Edge Board Supports Tensorflow Lite for Microcontrollers

Sparkfun Edge Tensorflow MCU Board

The 2019 TensorFlow Dev Summit is now taking place, and we’ve already covered the launch of Google’s Coral Edge TPU dev board and USB accelerator supporting TensorFlow Lite, but there has been another interesting new development during the event: TensorFlow Lite now also supports microcontrollers (MCU), instead of the more powerful application processors. You can easily get started with Tensorflow Lite for MCU with SparkFun Edge development board powered by Ambiq Micro Apollo3 Blue Bluetooth MCU whose ultra-efficient Arm Cortex-M4F core can run TensorFlow Lite using only 6uA/MHz. SparkFun Edge specifications: MCU – Ambiq Micro Apollo3 Blue 32-bit Arm Cortex-M4F processor at 48MHz / 96MHz (TurboSPOT) with DMA, 1MB flash, 384 KB SRAM, 6uA/MHz power usage, Bluetooth support. Connectivity – Bluetooth LE 5 (on-chip) + Bluetooth antenna Camera – OV7670 camera connector Audio – 2x MEMS microphones with operational amplifier Sensor – STMicro LIS2DH12 3-axis accelerometer Expansion – Qwiic connector, 4x GPIO header, Debugging – FTDI-style serial header for programming …

Google to Launch Edge TPU Powered Coral Development Board and USB Accelerator

Coral Dev Board

Several low power neural network accelerators have been launched over the recent years in order to accelerator A.I. workloads such as object recognition, and speech processing. Recent announcements include USB devices such as Intel Neural Compute Stick 2 or Orange Pi AI Stick2801. I completely forgot about it, but Google also announced their own Edge TPU ML accelerator, development kit, and USB accelerator last summer. The good news is that Edge TPU powered Coral USB accelerator and Coral dev board and are going to launch in the next few days for respectively $74.99 and $149.99. Coral Development Board Coral dev board is comprised of a base board and SoM wit the following specifications: Edge TPU Module SoC – NXP i.MX 8M quad core Arm Cortex-A53 processor with Arm Cortex-M4F real-time core,  GC7000 Lite 3D GPU ML accelerator – Google Edge TPU coprocessor delivering up to 4 TOPS System Memory – 1 GB LPDDR4 RAM Storage – 8 GB eMMC Flash …

Arm Helium Delivers up to 15x Performance Uplift for Machine Learning on Cortex-M MCUs

Arm Helium

Arm has just unveiled Armv8.1-M architecture that adds Arm Helium technology, the M-Profile Vector Extension (MVE) for the Arm Cortex-M cores that will improve the compute performance of Cortex-M based microcontrollers. Helium will deliver up to 15 times more machine learning (ML) performance and up to 5 times uplift to signal processing allowing local decision-making on low-power embedded devices. Helium instructions will enable new applications for Arm Cortex-M microcontrollers in audio devices, sensor hubs, keyword spotting, voice command control, power electronics, communications and still image processing. Helium and Neon (the Advanced SIMD technology for Arm Cortex-A processors) are similarities but Helium has been designed for efficient signal processing performance in small processors. One different illustrated below is that while NEON loads 128-bit instructions (e.g. VLDR, VLMA), Helium will split up 128‑bit wide instruction into four equally sized chunks, called “beats” (labelled A to D) due to difference between Cortex-M and Cortex-A cores as explained in details in an Arm Research …

96Boards AI Sophon Edge Developer Board Features Bitmain BM1880 ASIC SoC

96boards Sophon Edge

Bitmain, a company specializing in cryptocurrency, blockchain, and artificial intelligence (AI) application, has just joined Linaro, and announced the first 96Boards AI platform featuring an ASIC: Sophon BM1880 Edge Development Board, often just referred to as “Sophon Edge”. The board conforms to the 96Boards CE specification, and include two Arm Cortex-A53 cores, a Bitmain Sophon Edge TPU delivering 1 TOPS performance on 8-bit integer operations, USB 3.0 and gigabit Ethernet. Sophon Edge specifications: SoC ASIC – Sophon BM1880 dual core Cortex-A53 processor @ 1.5 GHz, single core RISC-V processor @ 1 GHz, 2MB on-chip RAM, and a TPU (Tensor Processing Unit) that can provide 1TOPS for INT8,and up to 2 TOPs by enabling Winograd convolution acceleration System Memory – 1GB LPDDR4 @ 3200Mhz Storage – 8GB eMMC flash + micro SD card slot Video Processing – H.264 decoder, MJPEG encoder/decoder, 1x 1080p @ 60fps or 2x 1080p @ 30fps H.264 decoder, 75fps for FHD images Connectivity – Gigabit Ethernet(RJ-45), Wifi, Bluetooth …

Bonsai Algorithm Enables Machine Learning on Arduino with a 2KB RAM Footprint

Bonsai Machine Learning

Machine learning used to be executed in the cloud, then the inference part  moved to the edge, and we’ve even seen micro-controllers able to do image recognition with GAP8 RISC-V micro-controller. But I’ve recently come across a white paper entitled “Resource-efficient Machine Learning in 2 KB RAM for the Internet of Things” that shows how it’s possible to perform such tasks with very little resources. Here’s the abstract: This paper develops a novel tree-based algorithm, called Bonsai, for efficient prediction on IoT devices – such as those based on the Arduino Uno board having an 8 bit ATmega328P microcontroller operating at 16 MHz with no native floating point support, 2 KB RAM and 32 KB read-only flash. Bonsai maintains prediction accuracy while minimizing model size and prediction costs by: (a) developing a tree model which learns a single, shallow, sparse tree with powerful nodes; (b) sparsely projecting all data into a low-dimensional space in which the tree is learnt; and …

$50 Kendryte KD233 Board Features K210 Dual Core RISC-V SoC

Kendryte KD223 RISC-V Board

RISC-V is talked about a lot, and we’re started to see a few development boards coming to market, or at least being announced with some based on SiFive processors such as HiFive Unleashed or Arduino Cinque, as well as other like GAPUINO GAP8 for low power A.I. applications. The Arduino board is not for sale yet, and HiFive Unleashed and GAPUINO GAP8 are fairly expensive at $999 and $229. Kendryte KD233 board is another RISC-V development board, based on  Kendryte K210 dual core 64-bit RISC-V processor designed for machine vision and “machine hearing”. The board goes for $49.99 on AnalogLamb. Kendryte KD233 board specifications: SoC – Kendryte K210 dual core 64-bit RISC-V processor, KPU  Convolutional Neural Network (CNN) hardware accelerator, APU audio hardware accelerator, 6MiB of on-chip general-purpose SRAM memory and 2MiB of on-chip AI SRAM memory, AXI ROM to load user program from SPI flash Storage – 128 Mbit SPI NOR flash, micro SD card slot Display – TFT …

NXP Unveils i.MX RT600 Series Arm Cortex-M33 + Audio DSP Crossover Processor

NXP IMX RT600

A little over a year ago, NXP introduces their first crossover processor that blurs the line between real-time capabilities of microcontrollers and higher performance of application processors with NXP i.MX RT1050 processor equipped with a Cortex-M7 core clocked at up to 700 MHz. The company has now announced another model with lower power consumption. NXP i.MX RT600 series comes with a Cortex M33 core clocked at up to 300MHz, a Cadence Tensillica HiFi 4 audio DSP, and up to 4.5MB shared SRAM. Main features of NXP i.MX RT685 crossover processor: CPU Core – Arm  Cortex-M33 up to 300 MHz DSP – Tensilica Hi-Fi 4 up to 600 MHz Memory Up to 4.5 MB on-chip RAM 128KB DSP TCM, 128 KB DSP Cache Storage 96KB ROM on-chip 2x SDIO with 1x supporting eMMC5.0 w/ HS400 1x Octal/Quad SPI up to 100MB/s Peripherals 2x DMA Engines with 35 channels each 1x USB high speed host/device with PHY 1x SPI  up to 50 …