Abstract Machine learning has become an indispensable part of the existing technological
domain. Edge computing and Internet of Things (IoT) together presents a new opportunity to …

The rapid growth in miniaturization of low-power embedded devices and advancement in
the optimization of machine learning (ML) algorithms have opened up a new prospect of the …

The advancements in machine learning (ML) opened a new opportunity to bring intelligence
to the low-end Internet-of-Things (IoT) nodes, such as microcontrollers. Conventional ML …

Resource-constrained IoT devices, such as sensors and actuators, have become ubiquitous
in recent years. This has led to the generation of large quantities of data in real-time, which …

Deep Neural Networks (DNNs) have emerged as the core enabler of many major
applications on mobile devices. To achieve high accuracy, DNN models have become …

We present CrypTFlow, a first of its kind system that converts TensorFlow inference code into
Secure Multi-party Computation (MPC) protocols at the push of a button. To do this, we build …

Complex machine learning (ML) inference algorithms like recurrent neural networks (RNNs)
use standard functions from math libraries like exponentiation, sigmoid, tanh, and reciprocal …

Machine learning (ML) models are widely used in many important domains. For efficiently
processing these computational-and memory-intensive applications, tensors of these …

This paper presents the Edge Learning Machine (ELM), a machine learning framework for
edge devices, which manages the training phase on a desktop computer and performs …

Currently, the applications of the Internet of Things (IoT) generate a large amount of sensor
data at a very high pace, making it a challenge to collect and store the data. This scenario …