On-device machine learning (ODML) enables powerful edge applications, but power consumption remains a key challenge for resource-constrained devices. To address this, developers often face a trade-off between model accuracy and power consumption, employing either computationally intensive models on high-power cores or pared-down models on low-power cores. Both approaches typically lead to a compromise in user experience (UX). This work focuses on the use of Gated Compression (GC) layer to enhance ODML model performance while conserving power and maximizing cost-efficiency, especially for always-on use cases. GC layers dynamically regulate data flow by selectively gating activations of neurons within the neural network and effectively filtering out non-essential inputs, which reduces power needs without compromising accuracy, and enables more efficient execution on heterogeneous compute cores. These improvements enhance UX through prolonged battery life, improved device responsiveness, and greater user comfort. In this work, we have integrated GC layers into vision and speech domain models including the transformer-based ViT model. Our experiments demonstrate theoretical power efficiency gains ranging from 158x to 30,000x for always-on scenarios. This substantial improvement empowers ODML applications with enhanced UX benefits.

In this blog post, we will learn to create a TensorFlow Lite model using the TF Lite Model Maker Library. We will fine-tune a pre-trained image classification model on the custom dataset and further explore different types of model optimization techniques currently supported by the library and export them to the TF Lite model. Detailed performance comparison of the created TF Lite models and the converted one is done, followed by deploying the model on the web app in the end. The TensorFlow Lite Model Maker Library enables us to train a pre-trained or a custom TensorFlow Lite model on a custom dataset. Similar to the previous blog, we will be using Microsoft's Cats and Dogs Dataset.

The recent trend in the development of larger and larger Deep Learning models for a slight increase in accuracy raises the concern about their computational efficiency and wide scaled usability. We can not use such huge models on resource-constrained devices like mobiles and embedded devices. Does it mean that such devices have to sacrifice accuracy at the cost of a smaller model? Is it possible at all to deploy these models on devices such as smartphones or a Raspberry Pi or even on Microcontrollers? Optimizing the models using TensorFlow Lite is the answer to these questions.

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On-Device Machine Learning is a project-based course covering how to build, train, and deploy models that can run on low-power devices (e.g. The course will cover advances topics on distillation, quantization, weight imprinting, power calculation and more. Every week we will discuss a new research paper and area in this space one day, and have a lab working-group the second. Specifically, students will be provided with low-power compute hardware (e.g. The project will involve three components for building low-power multimodal models: (1) inference (2) performing training/updates for interactive ML, and (3) maximizing power.

Google has introduced a new ML Kit SDK aimed at working in standalone mode without requiring a tight integration with Firebase, as the original ML Kit SDK did. Google has introduced a new ML Kit SDK aimed at working in standalone mode without requiring a tight integration with Firebase, as the original ML Kit SDK did. Additionally, it provides limited support for replacing its default models with custom ones for image labeling and object detection and tracking. Focusing ML Kit on on-device machine learning means your app will not experience any network latency and will be able to work offline. Additionally, the new ML Kit SDK keeps all of its data locally, which is a key requirement to build privacy-preserving applications.

Now that we have converted our model, we can focus on actually building our app. The entire source code is available on GitHub, so here I'm only going to focus on the most interesting parts. In the Python script, we specified (lines 6/7) that our model is going to take as input a bidimensional array of integers of shape [1, 64], i.e. something like this, where the inner array contains 64 elements: But what we're going to have in real life is a string, corresponding to the current text. We thus need to convert that string into integers, a.k.a. Roughly, we can say that a token is a numeral representation of a part of our string.

The current paradigm for using machine learning models on a device is to train a model in the cloud and perform inference using the trained model on the device. However, with the increasing number of smart devices and improved hardware, there is interest in performing model training on the device. Given this surge in interest, a comprehensive survey of the field from a device-agnostic perspective sets the stage for both understanding the state-of-the-art and for identifying open challenges and future avenues of research. Since on-device learning is an expansive field with connections to a large number of related topics in AI and machine learning (including online learning, model adaptation, one/few-shot learning, etc), covering such a large number of topics in a single survey is impractical. Instead, this survey finds a middle ground by reformulating the problem of on-device learning as resource constrained learning where the resources are compute and memory. This reformulation allows tools, techniques, and algorithms from a wide variety of research areas to be compared equitably. In addition to summarizing the state of the art, the survey also identifies a number of challenges and next steps for both the algorithmic and theoretical aspects of on-device learning.

Ten years ago, Google started a tradition of naming its Android after the names of deserts. However, the tradition has come to an end this year, when the search engine giant named its latest version of the OS as simply'Android 10'. On September 3, Google officially released the final version of Android 10 after several months in betas. Tech geeks are theorising that one of the reasons why Google stopped its desert tradition is due to the fact that Android is a global brand and going with a desert name that starts from Q leaves some regions out. However, it's just the name that has been switched; the statue of the robot will still be there.

With the emergence of batteryless computing platforms, we are now able to execute computer programs on embedded systems that do not require a dedicated energy source. These platforms are typically used in sensing applications [30, 39, 70, 73, 79], and their hardware architecture consists primarily of a sensor-enabled microcontroller that is powered by some form of harvested energy such as solar, RF or piezoelectric [63]. Programs that run on these platforms follow the so-called intermittent computing paradigm [50, 52, 75, 77] where a system pauses and resumes its code execution based on the availability of harvested energy. Over the past decade, the efficiency of batteryless computing platforms has been improved by reducing their energy waste through hardware provisioning, through check-pointing [64] to avoid restarting code execution from the beginning at each power-up [8], and through discarding stale sensor data [34] which are no longer useful. Despite these advancements, the capability of batteryless computing platforms has remained limited to simple sensing applications only. In this paper, we introduce the concept of intermittent learning (Figure 1) which makes energy harvested embedded systems capable of executing lightweight machine learning tasks. Their ability to run machine learning tasks inside energy harvesting microcontrollers pushes the boundary of batteryless computing as these devices are able to sense, learn, infer, and evolve over a prolonged lifetime. The proposed intermittent learning paradigm enables a true lifelong learning experience in mobile and embedded systems and advances sensor systems from being smart to smarter. Once deployed in the field, an intermittent learner classifies sensor data as well as learns from them to update the classifier at run-time--without requiring any help from any external system.