Microchip Technology (Nasdaq: MCHP) has recently announced its acquisition of Neuronix AI Labs to enhance its power-efficient, AI-enabled edge solutions deployed on field-programmable gate arrays (FPGAs).
Neuronix AI Labs specializes in neural networ
k sparsity optimization, a technology that reduces power consumption, size, and computational requirements for tasks like image classification, object detection, and semantic segmentation—while maintaining high accuracy.
Microchip’s mid-range PolarFire® FPGAs and SoCs are industry recognized for their low power consumption, reliability, and security. By integrating Neuronix’s technology, Microchip will be able to expand its portfolio to develop cost-effective, large-scale edge components optimized for computer vision applications that face size, cost, and power constraints. This acquisition will also enable a significant increase in AI/ML processing power on low- to mid-range FPGAs.
“The acquisition of Neuronix AI Labs’ technology 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.”
Additionally, this acquisition will make FPGA parallel processing more accessible to non-FPGA designers by leveraging industry-standard AI frameworks. Neuronix AI intellectual property, combined with Microchip’s compilers and software design kits, enables the implementation of AI/ML algorithms on customizable FPGA logic without requiring register-transfer level (RTL) expertise or deep knowledge of FPGA architecture. Moreover, it allows for continuous updates and upgrades to convolutional neural networks (CNNs) without the need to reprogram the 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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