Microchip Tightens AI Grit With New Buyout of Neuronix

Microchip Technology has acquired Neuronix AI Labs to expand its capabilities for power-efficient, AI-enabled edge solutions deployed on FPGAs.

Accordingly, Neuronix AI Labs provides neural network sparsity optimization technology. Therefore, enabling a reduction in power, size, and calculations. Particularly, for tasks such as image classification, object detection and semantic segmentation, while maintaining high accuracy.

Microchip’s mid-range PolarFire® FPGAs and SoCs already lead the industry in terms of low power consumption, reliability, and security capabilities. For that reason, the acquisition will enable Microchip to develop cost-effective, large-scale edge deployments of components. Especially, those designed for use in computer-vision applications on systems that have cost, size, and power constraints. At the same time, it enables a multifold increase in AI/ML processing horsepower on low and mid-range FPGAs.

Increases Neural Networking Capabilities

Bruce Weyer, corporate vice president of Microchip’s FPGA business unit considers the acquisition as a strategic to its FPGA and SoC line. Particularly, Weyer said, “The acquisition of Neuronix AI Labs’ technology will enhance our power efficiency for FPGAs and SoCs deployed in intelligent edge systems.”

“Neuronix technology combined with our VectorBlox™ design flow produces an increase in neural network performance efficiency. Thus, 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.”

The acquisition of this technology will allow non-FPGA designers to harness powerful parallel processing capabilities. Specifically, by using industry-standard AI frameworks without requiring in-depth knowledge of FPGA design flow. This allows the combination of Neuronix AI intellectual property and Microchip’s existing compilers and kits for AI/ML algorithms. Particularly, for customizable FPGA logic without a need for register-transfer level (RTL) expertise or intimate knowledge of the underlying FPGA fabric. It allows 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. (This) 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.”  

-22 April 2024-