Machine Learning requires an enormous amount of mathematical computation per second. Several architectures have been proposed to match the computation requirements and improve the calculation efficiency. Among these, the Systolic Array accelerators show promising results. These accelerators are composed of several Processing Elements (PEs), arranged on multiple symmetrical lines, which include a Multiply-And-Accumulate module. Specific multi-precision multipliers are increasingly popular since they can execute different precision multiplications and they can be integrated into Systolic Array accelerators. In this work, a multi-precision multiplier is proposed. The objective of the design is to build up a multiplier formed by combining small...
High speed and competent addition of various operands is an essential operation in the design any co...
A new approach has been used for optimized design of multipliers based upon the concepts of Vedic ma...
Implementing arithmetic-heavy applications such as filters or neural networks in FPGAs relies to a g...
Machine Learning requires an enormous amount of mathematical computation per second. Several archite...
The need to support various machine learning (ML) algorithms on energy-constrained computing devices...
As key building blocks for digital signal processing, image processing and deep learning etc, adders...
During the last decade of integrated electronic design ever more functionality has been integrated o...
The recent growth in microprocessor performance has been a direct result of designers exploiting dec...
International audienceThis paper describes a new accumulate-and-add multiplication algorithm. The me...
The performance of multiplication in terms of speed and power is crucial for many Digital Signal met...
A contemporary computer spends a large percentage of its time executing multiplication. Although con...
In recent years there has been a growing interest in hardware neural networks, which express many be...
The progress of high-speed, low-power, and regular-layout multipliers is a latest in research. The m...
In this dissertation, we address the design of multi-functional arithmetic units working with the mo...
High speed computing continues to inspire the researchers, especially in the more demanding areas li...
High speed and competent addition of various operands is an essential operation in the design any co...
A new approach has been used for optimized design of multipliers based upon the concepts of Vedic ma...
Implementing arithmetic-heavy applications such as filters or neural networks in FPGAs relies to a g...
Machine Learning requires an enormous amount of mathematical computation per second. Several archite...
The need to support various machine learning (ML) algorithms on energy-constrained computing devices...
As key building blocks for digital signal processing, image processing and deep learning etc, adders...
During the last decade of integrated electronic design ever more functionality has been integrated o...
The recent growth in microprocessor performance has been a direct result of designers exploiting dec...
International audienceThis paper describes a new accumulate-and-add multiplication algorithm. The me...
The performance of multiplication in terms of speed and power is crucial for many Digital Signal met...
A contemporary computer spends a large percentage of its time executing multiplication. Although con...
In recent years there has been a growing interest in hardware neural networks, which express many be...
The progress of high-speed, low-power, and regular-layout multipliers is a latest in research. The m...
In this dissertation, we address the design of multi-functional arithmetic units working with the mo...
High speed computing continues to inspire the researchers, especially in the more demanding areas li...
High speed and competent addition of various operands is an essential operation in the design any co...
A new approach has been used for optimized design of multipliers based upon the concepts of Vedic ma...
Implementing arithmetic-heavy applications such as filters or neural networks in FPGAs relies to a g...