Microchip buys Neuronix AI Labs to boost neural network capabilities
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Microchip Technology has acquired Neuronix AI Labs to expand its capabilities for power-efficient, AI-enabled edge systems deployed on field programmable gate arrays (FPGAs).
Neuronix AI Labs provides neural network sparsity optimization technology that enables a reduction in power, size and calculations for tasks such as image classification, object detection and semantic segmentation, while maintaining high accuracy.
Mid-range PolarFire® FPGAs and SoCs from Microchip already lead the industry in terms of low power consumption, reliability and security capabilities. The acquisition of this neural network sparsity optimization technology will enable Microchip to develop cost-effective, large-scale edge deployments of components designed for use in computer-vision applications on systems that have cost, size and power constraints and enable a multifold increase in AI/ML processing horsepower on low and mid-range FPGAs.
“The acquisition will enhance our power efficiency for FPGAs and SoCs deployed in intelligent edge systems that utilize AI/ML algorithms,” said Bruce Weyer, corporate vice president of Microchip’s FPGA business unit. “Neuronix technology combined with our VectorBlox™ design flow produces an increase in neural network performance efficiency and delivers outstanding GOPS/watt performance in our low-power PolarFire FPGAs and SoCs. Systems designers will now be able to architect and deploy small-footprint hardware that was previously difficult to build due to size, thermal or power constraints.”
Neural network sparsity optimization technology will allow non-FPGA designers to harness powerful parallel processing capabilities using industry-standard AI frameworks without requiring in-depth knowledge of FPGA design flow. The combination of Neuronix AI intellectual property and Microchip’s existing compilers and software design kits allows for AI/ML algorithms to be implemented on customizable FPGA logic without a need for resistor-transition level (RTL) expertise or intimate knowledge of the underlying FPGA fabric. It is also designed to allow for updating and upgrading CNNs on the fly without needing to reprogram hardware.
“Neuronix AI Labs has been laser-focused on producing best-in-class neural network acceleration architectures and algorithms that can transform user expectations of size, power, performance and cost,” said Yaron Raz, CEO of Neuronix AI Labs. “Joining the Microchip team offers us a unique opportunity to scale and align with an FPGA portfolio that has set industry standards for power efficiency.”
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