Artificial intelligence (AI) is a computer system or other machine able to perform tasks that ordinarily require human intelligence like planning, learning, reasoning, problem solving, knowledge representation, perception, motion, manipulation and even social intelligence and creativity.
To extend the reach of AI hardware, Seeed provides a wide selection of AI hardware focusing on Machine Learning and Computer Vision, Edge Computing, Speech Recognition & NLP and Neural Networks Acceleration to allow anyone to build AIOT projects. Here is our pick of pieces of kit worth your attention based on function:
There is a broad body of research in AI, much of which feeds into and complements each other.
Currently enjoying something of a resurgence, machine learning is where a computer system is fed large amounts of data, which it then uses to learn how to carry out a specific task, such as understanding speech or captioning a photograph.By Nick Heath | February 12, 2018 — 11:23 GMT (19:23 GMT+08:00)
Topic: Managing AI and ML in the Enterprise
Machine learning has been widely used in data processing, computer vision, natural language processing, biometrics, search engines, medical diagnostics, detection of credit card fraud, securities market analysis, and robotics. But what is it exactly?
- Machine learning is a part of the artificial intelligence research field, more specifically, in how to improve the performance of specific algorithms in empirical learning.
- Machine learning is the study of computer algorithms that can be automatically improved through experience.
- Machine learning is the use of data or past experience to optimize the performance standards of computer programs.
With the explosive growth of connected devices, combined with a demand for privacy/confidentiality, low latency and bandwidth constraints, AI models trained in the cloud increasingly need to be run at the edge.
Seeed is developing a Grove HAT for Edge Computing based on the
It is an ideal way to start Machine Learning studies with NVIDIA Jetson Nano.
The NVIDIA® Jetson Nano™ Developer Kit delivers the computing performance to run modern AI workloads at an unprecedented size, power, and cost. Developers, learners, and makers can now run AI frameworks and models for applications like image classification, object detection, segmentation, and speech processing. Find more information at the NVIDIA Jetson Nano’s official page.
Coral from Google – Another hot piece of kit tailored to AI machine learning
The Coral Dev Board is a single-board computer with a removable system-on-module (SOM) that contains eMMC, SOC, wireless radios, and Google’s Edge TPU. It’s perfect for IoT devices and other embedded systems that demand fast on-device machine learning inferencing.
You can use the Dev Board as a single-board computer for accelerated machine learning processing in a small form factor, or as an evaluation kit for the onboard SOM. The 40 mm × 48 mm SOM on the Dev Board is available at volume and it can be combined with custom PCB hardware using board-to-board connectors to integrate into products.
The SOM is based on NXP’s iMX8M system-on-chip (SOC), but its unique power comes from the Edge TPU coprocessor. The Edge TPU is a small ASIC designed by Google that provides high-performance machine learning inferencing with a low power cost. For example, it can execute state-of-the-art mobile vision models such as MobileNet v2 at 100+ fps, in a power efficient manner.
Edge computing brings memory and computing power closer to the location where it is needed.Edge computing
From Wikipedia, the free encyclopedia
Select the development board for your specific edge computing application:
- Processing and analyzing data at the edge of the network.
- More secure, real-time data analysis and intelligent processing
- Reducing energy consumption of the entire system
- Reducing the time for integrating and migrating data.
Hikey970 is the super edge AI computing platform powered by Kirin970 SOC with 4 x Cortex A73,4 x Cortex A53. Hikey970 has 6GB LPDDR4 RAM, 64GB UFS storage, Gigabit Ethernet, GPS,
With Plug and AI in mind, Horned Sungem is dedicated to
Speech Recognition & NLP
Natural Language Processing (NLP) refers to
NLP is required when building intelligent systems to ask robots to perform per given instructions.
ReSpeaker is an open modular voice interface to energize the world around you just using your voice. Interact with home appliances, plants, the office, internet-connected devices and other things in day-to-day life, with the power of speech. The ReSpeaker project provides hardware components and software libraries to build fully voice-enabled devices.
The below table we focused on the hardware specification and features to help you choose the most suitable development boards, modules and kits for you specific area projects.
|SoM/Soc/CPU||NPU/TPU/VPN/KPUperformance||Key HardwareFeatures||Supported OS/Framework/SDK/Languages|
AI Modules / Dev. Boards / Kits
|RISC-V Dual Core 64-bit @400MHz, independent FPU, KPU,APU||independent KPU
|3-channel DC-DC power8MB/16MB/128MB FlashWiFi chip ESP8285||OS: -Framework: Tiny YOLO / MobileNet v1 / TensorFlow LiteLanguages: Micropython / C++SDK: Maixpy / Standalone SDK / FreeRTOS SDK||Sipeed MAix-I Module|
|Sipeed MAix BiT|
|Sipeed M1w Dock Suit|
|Sipeed MAix GO Suit|
|Edge TPU Module / NXP i.MX 8M SOC||–||Integrated GC7000 Lite Graphics1 GB LPDDR48 GB eMMC802.11b/g/n/acGigabit Ethernet port3.5mm audio jack HDMI 2.0a (full size) 39-pin FFC connector||OS: Debian LinuxFramework: TensorFlow LiteLanguages: Python(C++ Coming soon)||Google Coral Dev Board with a removable SOM|
|Rockchip RK3399||Lightspeeur 2801S5.6 TOPS5.6 TOPS@100MHz2.8 TOPS@300mW||ARM Mali-T860 MP4 quad core GPUVPU Support 4K VP9 and 4K 10bits H265/H264 video decoding, up to 60 fpsGigabit Ethernet||Supported OS: Ubuntu18.04 / Debian9 / Linux+QT / xserver / wayland / Android 8.1Supported Framework: Pytorch / Caffe / TensorFlow LiteSupported Languages: Supported SDK: GTI SDK / Linux SDK / Android SDK / Firefly API||NCC S1+ ROC-RK3399-PC AI Package|
|GAP8||8 GOPS200 MOPS@1mW||RISC-V, Hardware Convolution Engine, ARDUINO form factor||Supported Framework: TensorFlowSupported Languages: C/ C++ / OpenMPSupported SDK: CMSIS API||GAPUINO GAP8 Developer Kit|
|HiSilicon Kirin 970||FP16: 1.92TFLOPS||6GB LPDDR4X 1866MHz1080p@60Hz HDMIBluetooth/WIFI/GPS||OS: Android / LinuxFramework: Caffe / TensorFlow / OpenCVLanguages: PythonSDK: HiAI SDK / HiAI API||HiKey 970 Development Board|
|XC7Z020-1CLG400C||–||TBD||TBD||PYNQ™ Z2 board – based on Xilinx Zynq C7Z020 SoC|
|RK3308||–||Support DDR3/NandFlash/eMMC/MicroSD/802.11 b/g/n/Bluetooth 4.2, built-in audio CODEC||OS: Amazon AVS / DuerOS / AliOS Things / ROSFramework: -Languages: C / PythonSDK: Firefly SDK / Buildroot / DuerOS / Aispeech / iflytek / Amazon Alexa||ROC-RK3308-CC Quad-Core 64-Bit AIOT Main Board|
AI Enablement for the Grove system is ongoing!
We have now updated the Grove.py Python library to support the Coral Dev Board and NVIDIA Jetson Nano. Connect over 200 Grove modules simply and easily with the new libraries.
Here is a blinking button demo with the Coral Dev board.
import time from grove.gpio
led = GPIO(12, GPIO.OUT)
button = GPIO(22, GPIO.IN)
In the near future, Seeed will also expand the Grove system to include Scenario kits with the community for real projects using Google’s Edge TPU! Keep in touch with us! Let us know what you want to see in the forum, and we will do our best to listen and take action!