Search Results for author: Forrest Iandola

Found 12 papers, 7 papers with code

SqueezeNAS: Fast neural architecture search for faster semantic segmentation

1 code implementation5 Aug 2019 Albert Shaw, Daniel Hunter, Forrest Iandola, Sammy Sidhu

For real time applications utilizing Deep Neural Networks (DNNs), it is critical that the models achieve high-accuracy on the target task and low-latency inference on the target computing platform.

Ranked #55 on Semantic Segmentation on Cityscapes val (using extra training data)

Image Classification Neural Architecture Search +1

DSCnet: Replicating Lidar Point Clouds with Deep Sensor Cloning

no code implementations17 Nov 2018 Paden Tomasello, Sammy Sidhu, Anting Shen, Matthew W. Moskewicz, Nobie Redmon, Gayatri Joshi, Romi Phadte, Paras Jain, Forrest Iandola

Recently, autonomous vehicles have created a demand for depth information, which is often obtained using hardware sensors such as Light detection and ranging (LIDAR).

Autonomous Vehicles Depth Estimation +4

Keynote: Small Neural Nets Are Beautiful: Enabling Embedded Systems with Small Deep-Neural-Network Architectures

no code implementations7 Oct 2017 Forrest Iandola, Kurt Keutzer

Over the last five years Deep Neural Nets have offered more accurate solutions to many problems in speech recognition, and computer vision, and these solutions have surpassed a threshold of acceptability for many applications.

speech-recognition Speech Recognition

SqueezeDet: Unified, Small, Low Power Fully Convolutional Neural Networks for Real-Time Object Detection for Autonomous Driving

13 code implementations4 Dec 2016 Bichen Wu, Alvin Wan, Forrest Iandola, Peter H. Jin, Kurt Keutzer

In addition to requiring high accuracy to ensure safety, object detection for autonomous driving also requires real-time inference speed to guarantee prompt vehicle control, as well as small model size and energy efficiency to enable embedded system deployment.

Autonomous Driving object-detection +1

How to scale distributed deep learning?

no code implementations14 Nov 2016 Peter H. Jin, Qiaochu Yuan, Forrest Iandola, Kurt Keutzer

Training time on large datasets for deep neural networks is the principal workflow bottleneck in a number of important applications of deep learning, such as object classification and detection in automatic driver assistance systems (ADAS).

General Classification

Boda-RTC: Productive Generation of Portable, Efficient Code for Convolutional Neural Networks on Mobile Computing Platforms

1 code implementation1 Jun 2016 Matthew Moskewicz, Forrest Iandola, Kurt Keutzer

Results are presented for a case study of targeting the Qualcomm Snapdragon 820 mobile computing platform for CNN deployment.

Code Generation

libHOG: Energy-Efficient Histogram of Oriented Gradient Computation

1 code implementation ITSC 2015 Forrest Iandola, Matthew Moskewicz, Kurt Keutzer

Histogram of Oriented Gradients (HOG) features are the underlying representation in automotive computer vision applications such as collision avoidance and lane keeping.

Object Detection

Deformable Part Models are Convolutional Neural Networks

1 code implementation CVPR 2015 Ross Girshick, Forrest Iandola, Trevor Darrell, Jitendra Malik

Deformable part models (DPMs) and convolutional neural networks (CNNs) are two widely used tools for visual recognition.

Object Detection

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