Best of both worlds: Automl codesign of a cnn and its hardware accelerator
Neural architecture search (NAS) has been very successful at outperforming human-
designed convolutional neural networks (CNN) in accuracy, and when hardware information …
designed convolutional neural networks (CNN) in accuracy, and when hardware information …
Fbnetv3: Joint architecture-recipe search using predictor pretraining
Abstract Neural Architecture Search (NAS) yields state-of-the-art neural networks that
outperform their best manually-designed counterparts. However, previous NAS methods …
outperform their best manually-designed counterparts. However, previous NAS methods …
Towards improving the consistency, efficiency, and flexibility of differentiable neural architecture search
Most differentiable neural architecture search methods construct a super-net for search and
derive a target-net as its sub-graph for evaluation. There exists a significant gap between the …
derive a target-net as its sub-graph for evaluation. There exists a significant gap between the …
Multinomial distribution learning for effective neural architecture search
Abstract Architectures obtained by Neural Architecture Search (NAS) have achieved highly
competitive performance in various computer vision tasks. However, the prohibitive …
competitive performance in various computer vision tasks. However, the prohibitive …
Distilling optimal neural networks: Rapid search in diverse spaces
Current state-of-the-art Neural Architecture Search (NAS) methods neither efficiently scale to
many hardware platforms nor handle diverse architectural search-spaces. To remedy this …
many hardware platforms nor handle diverse architectural search-spaces. To remedy this …
Co-exploration of neural architectures and heterogeneous asic accelerator designs targeting multiple tasks
Neural Architecture Search (NAS) has demonstrated its power on various AI accelerating
platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units …
platforms such as Field Programmable Gate Arrays (FPGAs) and Graphic Processing Units …
Rethinking co-design of neural architectures and hardware accelerators
Neural architectures and hardware accelerators have been two driving forces for the
progress in deep learning. Previous works typically attempt to optimize hardware given a …
progress in deep learning. Previous works typically attempt to optimize hardware given a …
Unas: Differentiable architecture search meets reinforcement learning
Neural architecture search (NAS) aims to discover network architectures with desired
properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has …
properties such as high accuracy or low latency. Recently, differentiable NAS (DNAS) has …
sharpdarts: Faster and more accurate differentiable architecture search
Neural Architecture Search (NAS) has been a source of dramatic improvements in neural
network design, with recent results meeting or exceeding the performance of hand-tuned …
network design, with recent results meeting or exceeding the performance of hand-tuned …
On neural architecture search for resource-constrained hardware platforms
In the recent past, the success of Neural Architecture Search (NAS) has enabled
researchers to broadly explore the design space using learning-based methods. Apart from …
researchers to broadly explore the design space using learning-based methods. Apart from …