Wepropose MCUNet, aframeworkthat jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference onmicrocontrollers.

We thank all the reviewers for their constructive comments. We will provide details in the final draft. MCUNet shows consistent improvement across different devices (F746, H743) and tasks (classification, detection). R1: Whether the overall network topology brings major improvement. R2: Why the auto-tuning in TVM fails to work on MCUs.

Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e.

TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model.

Machine learning on tiny IoT devices based on microcontroller units (MCU) is appealing but challenging: the memory of microcontrollers is 2-3 orders of magnitude smaller even than mobile phones. We propose MCUNet, a framework that jointly designs the efficient neural architecture (TinyNAS) and the lightweight inference engine (TinyEngine), enabling ImageNet-scale inference on microcontrollers. TinyNAS adopts a two-stage neural architecture search approach that first optimizes the search space to fit the resource constraints, then specializes the network architecture in the optimized search space. TinyNAS can automatically handle diverse constraints (i.e. TinyNAS is co-designed with TinyEngine, a memory-efficient inference library to expand the search space and fit a larger model.

This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new--and much smaller--places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the "internet of things" (IoT). The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

This branch of artificial intelligence curates your social media and serves your Google search results. Soon, deep learning could also check your vitals or set your thermostat. MIT researchers have developed a system that could bring deep learning neural networks to new – and much smaller – places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the IoT. The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security.

The researchers from Massachusetts Institute of Technology (MIT) have developed a system that could bring deep learning neural networks to new -- and much smaller -- places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the IoT. The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security. MCUNet has two components needed for "tiny deep learning" -- the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system.

The researchers from Massachusetts Institute of Technology (MIT) have developed a system that could bring deep learning neural networks to new -- and much smaller -- places, like the tiny computer chips in wearable medical devices, household appliances, and the 250 billion other objects that constitute the IoT. The system, called MCUNet, designs compact neural networks that deliver unprecedented speed and accuracy for deep learning on IoT devices, despite limited memory and processing power. The technology could facilitate the expansion of the IoT universe while saving energy and improving data security. MCUNet has two components needed for "tiny deep learning" -- the operation of neural networks on microcontrollers. One component is TinyEngine, an inference engine that directs resource management, akin to an operating system.