IEEE Circuits and Systems Magazine - Q3 2020 - 1

Circuits
and Systems
IEEE

MAGAZINE

Volume 20, Number 3 Third Quarter 2020

Features

TinyML-Enabled Frugal Smart Objects:
Challenges and Opportunities
Ramon Sanchez-Iborra and Antonio F. Skarmeta

The TinyML paradigm proposes to integrate Machine Learning (ML)-based mechanisms within small
objects powered by Microcontroller Units (MCUs). This paves the way for the development of novel
applications and services that do not need the omnipresent processing support from the cloud, which
is power consuming and involves data security and privacy risks. In this work, a comprehensive review
of the novel TinyML ecosystem is provided. The related challenges and opportunities are identified
and the potential services that will be enabled by the development of truly smart frugal objects are
discussed. As a main contribution of this paper, a detailed survey of the available TinyML frameworks
for integrating ML algorithms within MCUs is provided. Besides, aiming at illustrating the given discussion, a real case study is presented. Concretely, we propose a multi-Radio Access Network (RAT)
architecture for smart frugal objects. The issue of selecting the most adequate communication interface for sending sporadic messages considering both the status of the device and the characteristics
of the data to be sent is addressed. To this end, several TinyML frameworks are evaluated and the
performances of a number of ML algorithms embedded in an Arduino Uno board are analyzed. The
attained results reveal the validity of the TinyML approach, which successfully enables the integration
of techniques such as Neural Networks (NNs), Support Vector Machine (SVM), decision trees, or
Random Forest (RF) in frugal objects with constrained hardware resources. The outcomes also show
promising results in terms of algorithm's accuracy and computation performance.
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Introduction to Dynamic Stochastic Computing
Siting Liu, Warren J. Gross, and Jie Han

Stochastic computing (SC) is an old but reviving computing paradigm for its simple data path
that can perform various arithmetic operations. It allows for low power implementation, which
would otherwise be complex using the conventional positional binary coding. In SC, a number is
encoded by a random bit stream of '0's and '1's with an equal weight for every bit. However, a long
bit stream is usually required to achieve a high accuracy. This requirement inevitably incurs a long
latency and high energy consumption in an SC system. In this article, we present a new type of
stochastic computing that uses dynamically variable bit streams, which is, therefore, referred to as
dynamic stochastic computing (DSC). In DSC, a random bit is used to encode a single value from
a digital signal. A sequence of such random bits is referred to as a dynamic stochastic sequence.
Using a stochastic integrator, DSC is well suited for implementing accumulation-based iterative
algorithms such as numerical integration and gradient descent. The underlying mathematical
models are formulated for functional analysis and error estimation. A DSC system features a
higher energy efficiency than conventional computing using a fixed-point representation with a
power consumption as low as conventional SC. It is potentially useful in a broad spectrum of
applications including signal processing, numerical integration and machine learning.

High-Speed Printed Circuit Boards: A Tutorial
Mohammad Abu Khater

Circuit designers often tend to overlook Printed Circuit Board (PCB) designs. Circuits performance
and the quality of measurements, however, are strongly correlated with careful and comprehensive PCB design techniques. Such design techniques are applicable to practically all circuits areas
including analog, digital, RF, and power applications. This paper provides a fundamental understanding of common problems faced in designing high-performance and high-speed PCBs. While
this tutorial paper does not cover basics of PCBs, it presents empirical and commonly practiced
methods to deliver professional layouts. This includes studying on-board transmission lines and
their matching techniques, various PCB structures that ensure good signal integrity, and bypass
capacitors, for circuits up to 30 GHz. Conclusions and suggestions are justified with theoretical
analysis and supported by simulations and measurements.

Digital Object Identifier 10.1109/MCAS.2020.3005466

THIRD QUARTER 2020

IEEE CIRCUITS AND SYSTEMS MAGAZINE

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IEEE Circuits and Systems Magazine - Q3 2020

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