Summary of Deeploy: Enabling Energy-efficient Deployment Of Small Language Models on Heterogeneous Microcontrollers, by Moritz Scherer et al.
Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers
by Moritz Scherer, Luka Macan, Victor Jung, Philip Wiese, Luca Bompani, Alessio Burrello, Francesco Conti, Luca Benini
First submitted to arxiv on: 8 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Hardware Architecture (cs.AR)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the challenge of deploying Small Language Models (SLMs) on microcontroller (MCU)-class chips, which typically lack high-bandwidth off-chip main memory access. To achieve this, the authors introduce a novel Deep Neural Network (DNN) compiler called Deeploy, which generates highly-optimized C code requiring minimal runtime support. The resulting end-to-end deployment of SLMs on a multicore RISC-V (RV32) MCU augmented with ML instruction extensions and a hardware neural processing unit (NPU) demonstrates leading-edge energy efficiency and throughput. For instance, the paper reports an SLM trained on the TinyStories dataset achieving 490 microjoules per token in terms of energy consumption and 340 tokens per second in terms of throughput when running on an MCU-class device without external memory. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine being able to use super smart language models like those used in chatbots or search engines, but on tiny devices that can fit in your pocket. That’s what this paper is about. The problem is that these small devices don’t have enough space for the huge amounts of memory needed to run these language models. To solve this, the researchers developed a new way to write code that takes into account the limited resources of these small devices. They tested their approach on a tiny device and showed that it can run language models efficiently while using very little energy. This has big implications for how we use AI in our daily lives. |
Keywords
» Artificial intelligence » Neural network » Token
