BPH/LUTS

Enable Lightweight and Precision-Scalable Posit/IEEE-754 Arithmetic in RISC-V Cores for Transprecision Computing

While posit format offers superior dynamic range and accuracy for transprecision computing, its adoption in RISC-V processors is hindered by the lack of a unified solution for lightweight, precision-scalable, and IEEE-754 arithmetic compatible hardware implementation. To address these challenges, we enhance RISC-V processors by 1) integrating dedicated posit codecs into the original FPU for ligh...

Karatsuba Algorithm Revisited for 2D Convolution Computation Optimization.

Convolution plays a significant role in many scientific and technological computations, such as artificial intelligence and signal processing. Convolutional computations consist of many dot-product operations (multiplication-accumulation, or MAC), for which the Winograd algorithm is currently the most widely used method to reduce the number of MACs. The Karatsuba algorithm, since its introduction ...

70% Size, 100% Accuracy: Lossless LLM Compression for Efficient GPU Inference via Dynamic-Length Float

Large Language Models (LLMs) have grown rapidly in size, creating significant challenges for efficient deployment on resource-constrained hardware. ...

Neural Signal Compression using RAMAN tinyML Accelerator for BCI Applications

High-quality, multi-channel neural recording is indispensable for neuroscience research and clinical applications. Large-scale brain recordings ofte...

NeuraLUT-Assemble: Hardware-aware Assembling of Sub-Neural Networks for Efficient LUT Inference

Efficient neural networks (NNs) leveraging lookup tables (LUTs) have demonstrated significant potential for emerging AI applications, particularly w...

FPGA-Accelerated SpeckleNN with SNL for Real-time X-ray Single-Particle Imaging

We implement a specialized version of our SpeckleNN model for real-time speckle pattern classification in X-ray Single-Particle Imaging (SPI) using ...

A Quantitative Evaluation of Approximate Softmax Functions for Deep Neural Networks

The softmax function is a widely used activation function in the output layers of neural networks, responsible for converting raw scores into class ...

Neuromorphic Retina: An FPGA-based Emulator

Implementing accurate models of the retina is a challenging task, particularly in the context of creating visual prosthetics and devices. Notwithsta...

Discovering an Image-Adaptive Coordinate System for Photography Processing

Curve & Lookup Table (LUT) based methods directly map a pixel to the target output, making them highly efficient tools for real-time photography pro...

Efficient Neural Network Encoding for 3D Color Lookup Tables

3D color lookup tables (LUTs) enable precise color manipulation by mapping input RGB values to specific output RGB values. 3D LUTs are instrumental ...

LUTMUL: Exceed Conventional FPGA Roofline Limit by LUT-based Efficient Multiplication for Neural Network Inference

For FPGA-based neural network accelerators, digital signal processing (DSP) blocks have traditionally been the cornerstone for handling multiplicati...

Implementation of a machine learning model in acute coronary syndrome and stroke risk assessment for patients with lower urinary tract symptoms.

OBJECTIVE: The global population is aging and the burden of lower urinary tract symptoms (LUTS) is expected to increase. According to the National Hea...

Transurethral Cylindrical Water Sac Prostate Enlargement Surgery for the Treatment of Small-Volume Benign Prostatic Hyperplasia: A Retrospective Analysis.

AIM: To investigate the clinical efficacy of transurethral columnar balloon dilation of prostate (TUCBDP) in the treatment of small-volume benign pros...

Predictors of mid-term functional outcomes for robot-assisted Madigan simple prostatectomy: results of a multicentric series according to the BPH-6 achievement.

BACKGROUND: BPH-6 achievement remains an objective far to be evaluated for every technique currently available for the surgical management of bladder ...

Transitional zone prostate cancer: Performance of texture-based machine learning and image-based deep learning.

This study is aimed to explore the performance of texture-based machine learning and image-based deep-learning for enhancing detection of Transitional...

[Trans-Douglas Retzius' space-sparing robot-assisted simple prostatectomy for large-volume benign prostate hyperplasia].

OBJECTIVE: To report the safety and efficacy of trans-Douglas Retzius' space-sparing robot-assisted simple prostatectomy (RSS-RASP) in the treatment o...

[Multicenter comparison of complications after robot-assisted and open simple prostatectomy].

INTRODUCTION: Robot-assisted simple prostatectomy (RASP) is a relatively new minimally invasive procedure for surgical treatment to manage symptomatic...

[Laparoscopic adenomectomy: Evolution of the technique and results.].

OBJECTIVE: Laparoscopic adenomectomy (LA) is an effective procedure for the treatment of benign prostatic hyperplasia (BPH), which reproduces the surg...