Search Results for author: Anna Goldie

Found 13 papers, 3 papers with code

Delving into Macro Placement with Reinforcement Learning

no code implementations6 Sep 2021 Zixuan Jiang, Ebrahim Songhori, Shen Wang, Anna Goldie, Azalia Mirhoseini, Joe Jiang, Young-Joon Lee, David Z. Pan

In physical design, human designers typically place macros via trial and error, which is a Markov decision process.


A Full-Stack Search Technique for Domain Optimized Deep Learning Accelerators

no code implementations26 May 2021 Dan Zhang, Safeen Huda, Ebrahim Songhori, Kartik Prabhu, Quoc Le, Anna Goldie, Azalia Mirhoseini

The rapidly-changing deep learning landscape presents a unique opportunity for building inference accelerators optimized for specific datacenter-scale workloads.

Natural Language Processing Optical Character Recognition

Transferable Graph Optimizers for ML Compilers

no code implementations NeurIPS 2020 Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter Ma, Qiumin Xu, Hanxiao Liu, Phitchaya Mangpo Phothilimthana, Shen Wang, Anna Goldie, Azalia Mirhoseini, James Laudon

Most compilers for machine learning (ML) frameworks need to solve many correlated optimization problems to generate efficient machine code.

Placement Optimization with Deep Reinforcement Learning

no code implementations18 Mar 2020 Anna Goldie, Azalia Mirhoseini

Placement Optimization is an important problem in systems and chip design, which consists of mapping the nodes of a graph onto a limited set of resources to optimize for an objective, subject to constraints.


Generalized Clustering by Learning to Optimize Expected Normalized Cuts

no code implementations16 Oct 2019 Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini

We introduce a novel end-to-end approach for learning to cluster in the absence of labeled examples.

GDP: Generalized Device Placement for Dataflow Graphs

no code implementations28 Sep 2019 Yanqi Zhou, Sudip Roy, Amirali Abdolrashidi, Daniel Wong, Peter C. Ma, Qiumin Xu, Ming Zhong, Hanxiao Liu, Anna Goldie, Azalia Mirhoseini, James Laudon

Runtime and scalability of large neural networks can be significantly affected by the placement of operations in their dataflow graphs on suitable devices.

Policy Optimization by Local Improvement through Search

no code implementations25 Sep 2019 Jialin Song, Joe Wenjie Jiang, Amir Yazdanbakhsh, Ebrahim Songhori, Anna Goldie, Navdeep Jaitly, Azalia Mirhoseini

On the other end of the spectrum, approaches rooted in Policy Iteration, such as Dual Policy Iteration do not choose next step actions based on an expert, but instead use planning or search over the policy to choose an action distribution to train towards.

Imitation Learning reinforcement-learning

GAP: Generalizable Approximate Graph Partitioning Framework

1 code implementation2 Mar 2019 Azade Nazi, Will Hang, Anna Goldie, Sujith Ravi, Azalia Mirhoseini

Graph partitioning is the problem of dividing the nodes of a graph into balanced partitions while minimizing the edge cut across the partitions.

graph partitioning

A Hierarchical Model for Device Placement

no code implementations ICLR 2018 Azalia Mirhoseini, Anna Goldie, Hieu Pham, Benoit Steiner, Quoc V. Le, Jeff Dean

We introduce a hierarchical model for efficient placement of computational graphs onto hardware devices, especially in heterogeneous environments with a mixture of CPUs, GPUs, and other computational devices.

Machine Translation reinforcement-learning +1

Massive Exploration of Neural Machine Translation Architectures

12 code implementations EMNLP 2017 Denny Britz, Anna Goldie, Minh-Thang Luong, Quoc Le

Neural Machine Translation (NMT) has shown remarkable progress over the past few years with production systems now being deployed to end-users.

Machine Translation Translation

Generating High-Quality and Informative Conversation Responses with Sequence-to-Sequence Models

no code implementations EMNLP 2017 Louis Shao, Stephan Gouws, Denny Britz, Anna Goldie, Brian Strope, Ray Kurzweil

Sequence-to-sequence models have been applied to the conversation response generation problem where the source sequence is the conversation history and the target sequence is the response.

Response Generation Translation

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