小型语言模型(SLM)

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小模型,也是嵌入式的未来
3 6 Ke· 2025-08-22 01:29
Core Insights - Nvidia's recent research highlights that Small Language Models (SLM) are the future of intelligent agents, introducing their own SLM, Nemotron-Nano-9B-V2, which achieved top performance in benchmark tests [1] - The trend of SLM is also impacting the MCU and MPU sectors, indicating a shift towards more compact and efficient AI models [1] Summary by Sections Small Language Models (SLM) - SLM parameters range from millions to tens of billions, while Large Language Models (LLM) can have hundreds of billions to trillions of parameters [2] - SLMs are compressed versions of LLMs, utilizing techniques like knowledge distillation, pruning, and quantization to maintain accuracy while reducing size [2] - Examples of SLMs include Llama3.2-1B, Qwen2.5-1.5B, DeepSeek-R1-1.5B, SmolLM2-1.7B, Phi-3.5-Mini-3.8B, and Gemma3-4B, with sizes ranging from 1 billion to 40 billion parameters [2] Running SLM on MCUs and MPUs - Running SLMs on MCUs requires specific capabilities, including a Neural Processing Unit (NPU) to accelerate Transformer operations [3] - High bandwidth and tightly coupled memory configurations are essential for effective data transfer within the system [3] - The best-performing MCUs can provide up to 250 GOPS, but for generative AI, at least double this performance is needed [3] Aizip and Renesas Collaboration - Aizip partnered with Renesas to showcase efficient SLMs and AI agents on MPU for edge applications, integrating them into Renesas RZ/G2L and RZ/G3S boards [4] - Aizip's models, named Gizmo, range from 300 million to 2 billion parameters, providing LLM-like functionality in a compact form [4] - These SLMs enhance privacy, flexibility, and cost savings for edge applications, although challenges remain in ensuring accurate tool invocation on low-cost devices [4] Alif Semiconductor's Innovations - Alif Semiconductor launched the Ensemble E4, E6, and E8 MCUs, designed to support SLMs and generative AI models [6] - The Ensemble E4 MCU, featuring dual Arm Cortex-M55 cores, can perform high-efficiency object detection and image classification in milliseconds [6] - Alif claims to have a head start in the market, having released their first-generation products in 2021, while competitors are still on earlier versions [8] Future of SLM in Embedded Systems - SLMs are expected to revolutionize embedded systems by providing advanced AI capabilities in resource-constrained environments [9] - Major MCU manufacturers are increasingly focusing on integrating AI functionalities, with notable examples including STMicroelectronics, Infineon, TI, NXP, and ADI [9] - By the second half of 2025, advanced MCUs are anticipated to include AI features in their product lines, with a significant emphasis on NPUs supporting Transformer models [9]