Artificial Intelligence Solutions for the Network Edge
Future Electronics has a global team of specialist engineers with decades of experience in microcontroller, microprocessor and FPGA embedded processing applications.
They are available to help you select the best solution for your Edge AI application.
We offer Artificial Intelligence edge solutions from multiple manufacturers of IC products, modules/boards and development tools.
While developers may choose to perform AI inferencing in the cloud, where hardware resources are less constrained, there are often good reasons for embedded systems to perform inferencing locally at the network edge, including:
- Lower latency
- Simpler hardware with less maintenance
- Immunity to poor network connections
- No costs associated with network data transfer
- Security against malware and other network-borne attacks
Local inferencing may be performed on a range of devices which embedded developers are already familiar, including microcontrollers, microprocessors/SOMs/SBCs and FPGAs. Through analysis of time-series sensor data and utilization of neural network models, they enable a broad set of applications such as the detection or identification of objects, gestures, keyword phrases, and industrial system anomalies.
AI edge solutions are available from multiple manufacturers of IC products, modules/boards and development tools. Demonstrations are available today for the following manufacturers and product families:
|Processor solution with built-in peripherals for event-based applications||NXP, Renesas, STMicroelectronics|
|Advanced processor solution with more/faster cores and interfaces, larger memory, virtual machine support and ability to run Linux/Windows/Android||Microchip, NXP, Renesas, STMicroelectronics|
|Parallel processing solution with large number of high-speed/customizable interfaces and optional soft or hard processing cores||Lattice Semiconductor, Microchip (Microsemi), QuickLogic|
|SOMs/SBCs||Complete system on module or single board computer solution with microprocessor/FPGA, memory and power management for faster time to market|
|Development Tools||Software solutions for Cortex-M class microcontrollers to streamline collection or labeling of training data, optimize algorithms and validate code without data science expertise||QuickLogic/SensiML, Renesas, STMicroelectronics|
Solutions by Application
A list of applications and products that have been demonstrated to address them are included in the table below, where you can follow links to view demos and check stock. Choice of product will depend on specific application requirements including speed and accuracy.
|Microcontrollers & Tools||Microprocessors||FPGAs/SoCs|
|Predictive Maintenance / Condition Monitoring / Anomaly Detection|| |
|Activity Recognition|| |
|Gesture Detection|| |
|Sound Analysis|| |
|Key Phrase Detection / Voice Control|| |
|Face Expression Recognition|| |
|Face or Object Tracking / Onlooker Detection|
|Human or Object Detection|| |
|Human or Object Counting|
|Image or Object Classification / Recognition|
|2D/3D Facial Recognition / Identification|
Future Electronics Development Kits
In addition to manufacturer board platforms available for the individual devices listed above, these additional kits designed by Future Electronics System Design Centers (SDC) may be used in the development of your AI application.
|Avalanche Development Kit|| |
Platform with Microchip MPF300TS 300kLE PolarFire FPGA and VSC8531 Gigabit Ethernet PHY, Panasonic PAN9420 WiFi module, and Arduino/mikroBUS/PMod connectors, ideal for object detection and monitoring in smart embedded vision applications.
|Compagno Development Board|| |
STWIN development kit which provides training and valuable information about Machine Learning (ML). Designers can perform cloud air quality demos with Scriptr using this board.
|Northern Lights Demonstration Kit || |
Platform combines machine learning with vision and digital signal controller technologies to provide an all-in-one demonstration for object recognition-based motor control applications. It includes a Video Board with a color CMOS image sensor and a low cost, low power FPGA; a Display Module with color OLED panel; and a Motor Control Board with a digital signal controller.