IEEE Circuits and Systems Magazine - Q1 2018 - 1

Circuits
and Systems
IEEE

MAGAZINE

Volume 18, number 1

first Quarter 2018

Features

A Lower Bound for the Hardware
Complexity of FIR Filters
Alireza Mehrnia and Alan N. Willson, Jr.
shannon's channel capacity theorem states that in a communication channel the
available power budget (snr) dictates the maximum realizable data rate. a relevant
question for fir filters then is this: given a hardware budget, can we similarly define
a general upper bound on the performance capabilities of a realizable fir filter? and
if yes, how close are the hardware complexities of existing fir filter designs in relation to such performance limitations? herein, we investigate such questions and we
present a practical approach to derive a lower bound for the minimal hardware budget
required for a practical fir filter realization of a given set of target filter specifications.
We believe this to be an important insight for fully understanding the design complexities of fir filters and for properly managing their power consumption expectations.

image licensed by ingram publishing

Optimization Framework
29 AforMemristor-Based
Artificial Intelligence Applications
Sijia Liu, Yanzhi Wang, Makan Fardad, and Pramod K. Varshney

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memristors have recently received significant attention as device-level components for
building a novel generation of computing systems. these devices have many promising features, such as non-volatility, low power consumption, high density, and excellent
scalability. the ability to control and modify biasing voltages at memristor terminals
make them promising candidates to perform matrix-vector multiplications and solve
systems of linear equations. in this article, we discuss how networks of memristors
arranged in crossbar arrays can be used for efficiently solving optimization and machine
learning problems. We introduce a new memristor-based optimization framework that
combines the computational merit of memristor crossbars with the advantages of an
operator splitting method, the alternating direction method of multipliers (admm). here,
admm helps in splitting a complex optimization problem into subproblems that involve
the solution of systems of linear equations. the strength of this framework is shown
by applying it to linear programming, quadratic programming, and sparse optimization. in addition to admm, implementation of a customized power iteration method
for eigenvalue/eigenvector computation using memristor crossbars is discussed. the
memristor-based power iteration method can further be applied to principal component
analysis. the use of memristor crossbars yields a significant speed-up in computation,
and thus, we believe, has the potential to advance optimization and machine learning
research in artificial intelligence.

Printed in U.S.A.

Digital Object Identifier 10.1109/MCAS.2017.2770204

first Quarter 2018

ieee circuits and systems magazine

Table of Contents for the Digital Edition of IEEE Circuits and Systems Magazine - Q1 2018