Trends in Neural Architecture Search: Towards the Acceleration of Search

Acceleration of Subspace Learning Machine via Particle Swarm Optimization and Parallel Processing

Built upon the decision tree (DT) classification and regression idea, the subspace learning machine (SLM) has been recently proposed to offer higher performance in general classification and regression tasks. Its performance improvement is reached at the expense of higher computational complexity. In this work, we investigate two ways to accelerate SLM. First, we adopt the particle swarm optimization (PSO) algorithm to speed up the search of a discriminant dimension that is expressed as a linear combination of current dimensions. The search of optimal weights in the linear combination is compu…

基于FPGA的ML模型加速器综述

A survey on FPGA-based accelerator for ML models

本文综述了过去六年顶级FPGA会议的287篇论文，指出ML与FPGA技术日益融合，研究侧重于推理加速（81%）且以CNN为主，同时GNN等新兴模型呈增长趋势，为未来研究方向提供了见解。

The Causality Inference of Public Interest in Restaurants and Bars on COVID-19 Daily Cases in the US: A Google Trends Analysis

The COVID-19 coronavirus pandemic has affected virtually every region of the globe. At the time of conducting this study, the number of daily cases in the United States is more than any other country, and the trend is increasing in most of its states. Google trends provide public interest in various topics during different periods. Analyzing these trends using data mining methods might provide useful insights and observations regarding the COVID-19 outbreak. The objective of this study was to consider the predictive ability of different search terms (i.e., bars and restaurants) with regards to…

ARM 4-BIT PQ: SIMD-based Acceleration for Approximate Nearest Neighbor Search on ARM

We accelerate the 4-bit product quantization (PQ) on the ARM architecture. Notably, the drastic performance of the conventional 4-bit PQ strongly relies on x64-specific SIMD register, such as AVX2; hence, we cannot yet achieve such good performance on ARM. To fill this gap, we first bundle two 128-bit registers as one 256-bit component. We then apply shuffle operations for each using the ARM-specific NEON instruction. By making this simple but critical modification, we achieve a dramatic speedup for the 4-bit PQ on an ARM architecture. Experiments show that the proposed method consistently ach…

O-HAS: Optical Hardware Accelerator Search for Boosting Both Acceleration Performance and Development Speed

The recent breakthroughs and prohibitive complexities of Deep Neural Networks (DNNs) have excited extensive interest in domain-specific DNN accelerators, among which optical DNN accelerators are particularly promising thanks to their unprecedented potential of achieving superior performance-per-watt. However, the development of optical DNN accelerators is much slower than that of electrical DNN accelerators. One key challenge is that while many techniques have been developed to facilitate the development of electrical DNN accelerators, techniques that support or expedite optical DNN accelerato…

Google Trends Analysis of COVID-19

The World Health Organization (WHO) announced that COVID-19 was a pandemic disease on the 11th of March as there were 118K cases in several countries and territories. Numerous researchers worked on forecasting the number of confirmed cases since anticipating the growth of the cases helps governments adopting knotty decisions to ease the lockdowns orders for their countries. These orders help several people who have lost their jobs and support gravely impacted businesses. Our research aims to investigate the relation between Google search trends and the spreading of the novel coronavirus (COVID…

NPAS: A Compiler-aware Framework of Unified Network Pruning and Architecture Search for Beyond Real-Time Mobile Acceleration

With the increasing demand to efficiently deploy DNNs on mobile edge devices, it becomes much more important to reduce unnecessary computation and increase the execution speed. Prior methods towards this goal, including model compression and network architecture search (NAS), are largely performed independently and do not fully consider compiler-level optimizations which is a must-do for mobile acceleration. In this work, we first propose (i) a general category of fine-grained structured pruning applicable to various DNN layers, and (ii) a comprehensive, compiler automatic code generation fram…

The Proper Use of Google Trends in Forecasting Models

It is widely known that Google Trends have become one of the most popular free tools used by forecasters both in academics and in the private and public sectors. There are many papers, from several different fields, concluding that Google Trends improve forecasts' accuracy. However, what seems to be widely unknown, is that each sample of Google search data is different from the other, even if you set the same search term, data and location. This means that it is possible to find arbitrary conclusions merely by chance. This paper aims to show why and when it can become a problem and how to over…

BEANNA: A Binary-Enabled Architecture for Neural Network Acceleration

Modern hardware design trends have shifted towards specialized hardware acceleration for computationally intensive tasks like machine learning and computer vision. While these complex workloads can be accelerated by commercial GPUs, domain-specific hardware is far more optimal when needing to meet the stringent memory, throughput, and power constraints of mobile and embedded devices. This paper proposes and evaluates a Binary-Enabled Architecture for Neural Network Acceleration (BEANNA), a neural network hardware accelerator capable of processing both floating point and binary network layers. …