The field of Tiny Machine Learning (TinyML) has gained significant attention due to its
potential to enable intelligent applications on resource-constrained devices. This review …

(57) ABSTRACT A method for selecting bit widths for a fixed point machine learning model
includes evaluating a sensitivity of model accuracy to bit widths at each computational stage …

Machine Learning (ML) functions are becoming ubiquitous in latency-and privacy-sensitive
IoT applications, prompting a shift toward near-sensor processing at the extreme edge and …

One of the most important stages of floating-point to fixed-point conversion, is the evaluation
of the fixed-point specification accuracy. This evaluation is required to optimize the data …

The design of digital hardware controllers for the integration of renewable energy sources in
DC microgrids is a research topic of interest. In this paper, a Kalman filter-based maximum …

From its introduction in the last decade, affine arithmetic (AA) has shown beneficial
properties to speed up the time of computation procedures in a wide variety of areas. In the …

Digital signal processing applications are specified with floating-point data types but they
are usually implemented in embedded systems with fixed-point arithmetic to minimize cost …

Division is an operation extensively used in architectures for digital signal processing
algorithms, which in portable devices require an implementation using fixed-point format. In …

Most of digital signal processing applications are specified and designed with floatingpoint
arithmetic but are finally implemented using fixed-point architectures. Thus, the design flow …

To reduce the gap between the VLSI technology capability and the designer productivity,
design reuse based on IP (intellectual properties) is commonly used. In terms of arithmetic …