Supports TensorFlow Lite and Lattice Propel for Embedded Processor-based Designs;
Includes New Lattice sensAI Studio Tool for Easy ML Model Training
Lattice Semiconductor Corporation, the low power programmable leader, announced enhancements to its award-winning Lattice sensAI solution stack for accelerating AI/ML application development on low power Lattice FPGAs. Enhancements include support for the Lattice Propel™ design environment for embedded processor-based development and the TensorFlow Lite deep-learning framework for on-device inferencing. The new version includes the Lattice sensAI Studio design environment for end-to-end ML model training, validation, and compilation. With sensAI 4.0, developers can use a simple drag-and-drop interface to build FPGA designs with a RISC-V processor and a CNN acceleration engine to enable the quick and easy implementation of ML applications on power-constrained Edge devices.
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“Lattice’s low-power FPGAs for embedded vision and sensAI solution stack for Edge AI/ML applications play a vital role in helping us bring leading-edge intelligent IoT devices to market quickly and efficiently”
There is growing demand in multiple end markets to add support for low power AI/ML inferencing for applications like object detection and classification. AI/ML models can be trained to support applications for a range of devices that require low-power operation at the Edge, including security and surveillance cameras, industrial robots, and consumer robotics and toys. The Lattice sensAI solution stack helps developers rapidly create AI/ML applications that run on flexible, low power Lattice FPGAs.
“Lattice’s low-power FPGAs for embedded vision and sensAI solution stack for Edge AI/ML applications play a vital role in helping us bring leading-edge intelligent IoT devices to market quickly and efficiently,” said Hideto Kotani, Unit Executive, Canon Inc.
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“With support for TensorFlow Lite and the new Lattice sensAI Studio, it’s now easier than ever for developers to leverage our sensAI stack to create AI/ML applications capable of running on battery-powered Edge devices,” said Hussein Osman, Marketing Director, Lattice.
Enhancements to the Lattice sensAI solution stack 4.0 include:
- TensorFlow Lite – support for the framework reduces power consumption and increases data co-processing performance in AI/ML inferencing applications. TensorFlow Lite runs anywhere from 2 to 10 times faster on a Lattice FPGA than it does on an ARM® Cortex®-M4-based MCU.
- Lattice Propel – the stack supports the Propel environment’s GUI and command-line tools to create, analyze, compile, and debug both the hardware and software design of an FPGA-based processor system. Even developers unfamiliar with FPGA design can use the tool’s easy-to-use, drag-and-drop interface to create AI/ML applications on low power Lattice FPGAs with support for RISC-V-based co-processing.
- Lattice sensAI Studio – a GUI-based tool for training, validating, and compiling ML models optimized for Lattice FPGAs. The tool makes it easy to take advantage of transfer learning to deploy ML models.
- Improved performance – by leveraging advances in ML model compression and pruning, sensAI 4.0 can support image processing at 60 FPS with QVGA resolution or 30 FPS with VGA resolution.
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