- The Ensemble E4/E6/E8 MCUs and fusion processors feature the leading accelerator for edge AI, Arm Ethos-U85 NPU, integrated ISP, and a wide memory bus to facilitate image capturing and buffering.
- In an industry first for microcontrollers, Alif's class leading system architecture includes hardware acceleration for transformer networks, requiring only 36mW for small language model (SLM) execution.
- Alif's hardware acceleration also sets new standards for imaging applications, performing power efficient object detection in less than 2ms, and image classification in less than 8ms.
PLEASANTON, Calif., Aug. 12, 2025 /PRNewswire/ -- Alif Semiconductor®, the leading global supplier of secure, connected, power efficient Artificial Intelligence and Machine Learning (AI/ML) microcontrollers (MCUs) and fusion processors, today released benchmarks on its latest E4, E6 and E8 microcontrollers and fusion processors, extending Alif's advantage in the AI MCU market.
Alif has been a trendsetter in this field since 2021, when it introduced its neural processing unit (NPU)-enabled MCUs to market. Today Alif further demonstrates its leadership in Edge AI with its latest series of Ensemble MCUs. The E4, E6 and E8 are all capable of performing generative AI workloads locally. In addition to this, they contain other new features that make them ideal for high performance data processing and imaging tasks.
The new Ensemble devices contain support for up to two MIPI-CSI image sensors. They also include a fully hardware-accelerated image signal processor (ISP) pipeline that operates at up to 60fps at 2MP resolution. This image throughput is enabled with a new wide memory subsystem for extremely fast on-chip as well as off-chip transactions, pushing inferencing speed well below a millisecond when running AI models from internal, highly power efficient MRAM.
The generative AI capability in the Ensemble E4, E6, and E8 devices is made possible by Alif's future-proof system architecture. Alif is the first silicon provider to offer the Arm® Ethos-U85 NPU which supports transformer-based ML networks. As an example of power efficiency, an SLM executed on an E4 device draws only 36mW of power when generating text to construct a story based on a user-provided prompt.
These levels of performance and efficiency enable developers to innovate next-generation products in human-to-computer interfacing, healthcare and diagnostics, robotics, transportation, toys and education, smart homes, and smart city equipment.
Reza Kazerounian, President of Alif Semiconductor, said: "With the E4, E6 and E8 series of Ensemble GenAI products, Alif continues to push the envelope of edge AI applications. While existing market solutions are built for real-time control, and not for AI, Alif built an AI-ready architecture from the start. That's why Alif customers are now able to use the E4, E6 and E8 devices to implement transformer-based models and generative AI in edge and endpoint products powered by a small battery."
Paul Williamson, Senior Vice President and General Manager, IoT Line of Business at Arm, said: "Generative AI is raising the bar for intelligence beyond the cloud, demanding greater performance, privacy, responsiveness, and efficiency. Powered by Arm Ethos technology and supported by our robust software ecosystem, Alif's latest Ensemble MCUs bring advanced on-device AI capabilities to even the most constrained devices, unlocking real-time insights in applications like health monitoring and wearables. This enables developers to deliver the next generation of intelligent, on-device experiences."
About Alif Semiconductor:
Alif Semiconductor is the industry-leading supplier of next-generation secure AI/ML-enabled 32-bit microcontrollers. Since 2019, Alif's expanding offering of microcontrollers and fusion processors has been revolutionizing the way developers can create broad, scalable, and connected AI-enabled embedded applications that are genuinely power efficient. Alif Semiconductor is the only choice for power efficient microcontrollers that can handle heavy AI and ML workloads for battery-operated IoT devices. For more information go to https://alifsemi.com/.
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