Enhancements to version 4.0 of the award-winngin Lattice sensAI solution stack include support for the Lattice Propel design environment and the Lattice sensAI Studio design environment for end-to-end ML model training, validation, and compilation.
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Lattice Semiconductor and Mistral Solutions have created a real-time data capture card compatible with Texas Instruments mmWave radar sensors.
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Read the latest comms and computing news from Lattice: Low power wireless Heterogenous Networks development and upgrade security with Lattice’s MachXO3D FPGA.
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Read the latest consumer news from Lattice: Lattice sensAI brings more performance to the edge, Radiant IP and reference design, demo videos from Embedded Vision Summit, industry award and press coverage for sensAI
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If you missed the 2019 Embedded Vision Summit, check out the latest sensAI demos from Lattice to and see what smart vision can enable in Edge devices
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A year ago we launched the Lattice sensAI solutions stack. Since then, the need for AI at the Edge has continued to grow. Consider this statistic from Tractica: by 2025 the market for Edge-based AI chipsets is forecasted to hit $51.6 billion (that’s over three times their forecasted revenues for cloud-based AI chips). Why all the interest in chips that support AI at the Edge?
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You may be asking yourself, “What does a nineteenth century architect have to do with FPGAs?” A key decision facing many system architects is which FPGA families to use in their next project. Lattice currently offers four classes of FPGAs: iCE, ECP, Mach and CrossLink. It is often tempting to think of FPGAs as just a blank canvas, ready to be filled with digital logic.
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Imagine you’ve been given the responsibility to develop a new video bridging solution. Initially, you have a specific application in mind, but as cameras emerge in a growing number of intelligent applications, ranging from object recognition and depth perception to lane detection and collision avoidance, you know you will need prototypes for a wider variety of functions.
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