FPGAs have distinct advantages as a technology for deploying deep neural networks (DNNs) at the edge. Lookup Table (LUT) based networks, where neurons are directly modelled using LUTs, help maximize this promise of offering ultra-low latency and high area efficiency on FPGAs. Unfortunately, LUT resource usage scales exponentially with the number of inputs to the LUT, restricting PolyLUT to small LUT sizes. This work introduces PolyLUT-Add, a technique that enhances neuron connectivity by combining $A$ PolyLUT sub-neurons via addition to improve accuracy. Moreover, we describe a novel architecture to improve its scalability. We evaluated our implementation over the MNIST, Jet Substructure classification and Network Intrusion Detection benchmark and found that for similar accuracy, PolyLUT-Add achieves a LUT reduction of $1.3-7.7\times$ with a $1.2-2.2\times$ decrease in latency.

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.

This post was originally published at thinkmobile.dev Looking for how to automatically test TensorFlow Lite model on a mobile device? Check the 2nd part of this article. Building TensorFlow Lite models and deploying them on mobile applications is getting simpler over time. There is a set of information that needs to be passed between those steps -- model input/output shape, values format, etc.

Edge computing devices are becoming the logical destination to run deep learning models. While the public cloud is the preferred environment for training, it is the edge that runs the models for inferencing. Since most of the edge devices have constraints in the form of available CPU and GPU resources, there are purpose-built AI chips designed to accelerate the inferencing. These AI accelerators complement the CPU by speeding up the calculations involved in inferencing. They are designed to optimize the forward propagation of neural networks deployed on the edge.

The rise of industrial Internet of Things (IoT) and artificial intelligence (AI) are making edge computing significant for enterprises. Many industry verticals such as manufacturing, healthcare, automobile, transportation, and aviation are considering an investment in edge computing. Edge computing is fast becoming the conduit between the devices that generate data and the public cloud that processes the data. In the context of machine learning and artificial intelligence, the public cloud is used for training the models and the edge is utilized for inferencing. To accelerate ML training in the cloud, public cloud vendors such as AWS, Azure, and the Google Cloud Platform (GCP) offer GPU-backed virtual machines.