FPGA HLS today: successes, challenges, and opportunities
The year 2011 marked an important transition for FPGA high-level synthesis (HLS), as it
went from prototy** to deployment. A decade later, in this article, we assess the progress …
went from prototy** to deployment. A decade later, in this article, we assess the progress …
Neural architecture search survey: A hardware perspective
We review the problem of automating hardware-aware architectural design process of Deep
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Neural Networks (DNNs). The field of Convolutional Neural Network (CNN) algorithm design …
Hardware/software co-exploration of neural architectures
We propose a novel hardware and software co-exploration framework for efficient neural
architecture search (NAS). Different from existing hardware-aware NAS which assumes a …
architecture search (NAS). Different from existing hardware-aware NAS which assumes a …
Confuciux: Autonomous hardware resource assignment for dnn accelerators using reinforcement learning
DNN accelerators provide efficiency by leveraging reuse of activations/weights/outputs
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …
during the DNN computations to reduce data movement from DRAM to the chip. The reuse is …
FracBNN: Accurate and FPGA-efficient binary neural networks with fractional activations
Binary neural networks (BNNs) have 1-bit weights and activations. Such networks are well
suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory …
suited for FPGAs, as their dominant computations are bitwise arithmetic and the memory …
Applications, databases and open computer vision research from drone videos and images: a survey
Analyzing videos and images captured by unmanned aerial vehicles or aerial drones is an
emerging application attracting significant attention from researchers in various areas of …
emerging application attracting significant attention from researchers in various areas of …
A comprehensive survey on hardware-aware neural architecture search
H Benmeziane, KE Maghraoui, H Ouarnoughi… - ar** object detection and tracking on resource-constrained embedded systems is
challenging. While object detection is one of the most compute-intensive tasks from the …
challenging. While object detection is one of the most compute-intensive tasks from the …
AutoDNNchip: An automated DNN chip predictor and builder for both FPGAs and ASICs
Recent breakthroughs in Deep Neural Networks (DNNs) have fueled a growing demand for
domain-specific hardware accelerators (ie, DNN chips). However, designing DNN chips is …
domain-specific hardware accelerators (ie, DNN chips). However, designing DNN chips is …