Current-generation Deep Neural Networks (DNNs), such as AlexNet and VGG, rely heavily
on dense floating-point matrix multiplication (GEMM), which maps well to GPUs (regular …

We propose a novel approach to learn and recognize natural scene categories. Unlike
previous work, it does not require experts to annotate the training set. We represent the …

In this paper, we address the question of how to automatically map computational kernels to
highly efficient code for a wide range of computing platforms and establish the correctness of …

Although quadrotors, and aerial robots in general, are inherently active agents, their
perceptual capabilities in literature so far have been mostly passive in nature. Researchers …

The implementation of Molecular Dynamics (MD) on FPGAs has received substantial
attention. Previous work, however, has consisted of either proof-of-concept implementations …

Technology scaling has created a catch-22: technology now can do almost anything we
want, but the NRE design costs are so high, that almost no one can afford to use it. Our …

Over the past decades, we noticed huge advances in FPGA technologies. The topic of
floating-point accelerator on FPGA has gained renewed interests due to the increased …

The 3D FFT is critical in many physical simulations and image processing applications. On
FPGAs, however, the 3D FFT was thought to be inefficient relative to other methods such as …

This paper presents the hardware architecture of an OFDM receiver suitable for optical
communications. The receiver has been implemented in an FPGA device and used to …

This paper introduces a method to compute the FFT of a visual scene at a high temporal
precision of around 1-μs output from an asynchronous event-based camera. Event-based …