The FloPoCo open-source arithmetic core generator project started modestly in 2008 with a
few parametric floating point cores. It has since then evolved to become a framework for …

This paper proposes an energy-efficient approximate multiplier which combines radix-4
Booth encoding and logarithmic product approximation. Additionally, a datapath pruning …

Arithmetic is a key component and is ubiquitous in today's digital world, ranging from
embedded to high-performance computing systems. With machine learning at the fore in a …

The design of a multiplier typically consists of three steps:(1) partial product generation,(2)
compressor tree design and (3) the selection of the final adder. Conventionally, these three …

High-fan-in dot product computations are ubiquitous in highly relevant application domains,
such as signal processing and machine learning. Particularly, the diverse set of data formats …

Matrix-matrix multiplication is a key computational kernel for numerous applications in
science and engineering, with ample parallelism and data locality that lends itself well to …

Convolutional neural networks (CNNs) gained great success in machine learning
applications and much attention was paid to their acceleration on field programmable gate …

This work presents an extension of Karatsuba's method to efficiently use rectangular
multipliers as a base for larger multipliers. The rectangular multipliers that motivate this work …

Field-programmable gate array (FPGA)–based accelerators are becoming increasingly
popular for deep neural network (DNN) inference due to their ability to scale performance …

Resource requirements for hardware acceleration of neural networks inference is
notoriously high, both in terms of computation and storage. One way to mitigate this issue is …