Deep learning models owe their initial success to large servers with large amounts of memory and clusters of GPUs. The promises of deep learning gave rise to an entire industry of cloud computing services for deep neural networks. Consequently, very large neural networks running on virtually unlimited cloud resources became very popular, especially among wealthy tech companies that can foot the bill.
But at the same time, recent years have also seen a reverse trend, a concerted effort to create machine learning models for edge devices. Called tiny machine learning, or TinyML, these models are suited for devices that have limited memory and processing power, and in which internet connectivity is either non-present or limited.
The latest in these efforts, a joint work by IBM and the Massachusetts Institute of Technology (MIT), addresses the peak-memory limit found in convolutional neural networks (CNN), a deep learning architecture that is especially critical for computer vision applications. Detailed in a paper presented at the NeurIPS 2021 conference, the model is called MCUNetV2 and can run complicated CNNs on microcontrollers with very little memory.
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