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Generative AI workloads are breaking every aspect of the data center. As the capabilities of AI increase, so does its demand. The conventional path of performance improvement in legacy processors has stagnated, providing diminishing returns. This raises concerns on whether we may ever fully realize the potential of AI. In this talk we will share how combining our software-first methodology and novel computer arithmetic leads to breakthrough performance gains in general purpose accelerated computing, pushing developers to the limit of physics.

Author:

Jay Dawani

CEO
Lemurian Labs

Jay Dawani is co-founder & CEO of Lemurian Labs, a startup at the forefront of general purpose accelerated computing for making AI development affordable and generally available for all companies and people to equally benefit. Author of the influential book "Mathematics for Deep Learning", he has held leadership positions at companies such as BlocPlay and Geometric Energy Corporation, spearheading projects involving quantum computing, metaverse, blockchain, AI, space robotics, and more. Jay has also served as an advisor to NASA Frontier Development Lab, SiaClassic, and many leading AI firms.

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Jay Dawani

CEO
Lemurian Labs

Jay Dawani is co-founder & CEO of Lemurian Labs, a startup at the forefront of general purpose accelerated computing for making AI development affordable and generally available for all companies and people to equally benefit. Author of the influential book "Mathematics for Deep Learning", he has held leadership positions at companies such as BlocPlay and Geometric Energy Corporation, spearheading projects involving quantum computing, metaverse, blockchain, AI, space robotics, and more. Jay has also served as an advisor to NASA Frontier Development Lab, SiaClassic, and many leading AI firms.

Author:

Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

... read more

Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

Author:

Vikas Chandra

Senior Director, AI
Meta

Vikas Chandra is Senior Director at Meta Reality Labs where he works on AI research focusing on AR and VR products. Prior to Meta, he was Director of Applied Machine Learning at Arm Inc. He received his Ph.D. and M.S. degrees in Electrical and Computer Engineering from Carnegie Mellon University. He held the positions of Visiting Scholar (2011 – 2014) and Visiting Faculty (2016 - 2017) in the EE department at Stanford University. He has authored 120+ research publications and is an inventor on 40+ US and international patents. Dr. Chandra received the ACM-SIGDA Technical Leadership Award in 2009 and was invited to the 2017 Frontiers of Engineering Symposium organized by the National Academy of Engineering. He is a senior member of IEEE.

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Vikas Chandra

Senior Director, AI
Meta

Vikas Chandra is Senior Director at Meta Reality Labs where he works on AI research focusing on AR and VR products. Prior to Meta, he was Director of Applied Machine Learning at Arm Inc. He received his Ph.D. and M.S. degrees in Electrical and Computer Engineering from Carnegie Mellon University. He held the positions of Visiting Scholar (2011 – 2014) and Visiting Faculty (2016 - 2017) in the EE department at Stanford University. He has authored 120+ research publications and is an inventor on 40+ US and international patents. Dr. Chandra received the ACM-SIGDA Technical Leadership Award in 2009 and was invited to the 2017 Frontiers of Engineering Symposium organized by the National Academy of Engineering. He is a senior member of IEEE.


This talk delves into the application of a hybrid cascaded architecture for optimized wakeword detection, focusing on its implementation in Roku's Voice Remote Pro. The importance of accurate wakewords for handsfree operation is introduced, followed by a discussion on how the hybrid cascaded architecture addresses the challenges in wakeword detection. These challenges include accuracy, low latency, low power consumption, noisy environments, and different pronunciations. A hybrid approach, which combines edge and cloud models, is presented as a solution to effectively manage these challenges. The cascaded architecture, a two-stage process involving a remote keyword spotter and a cloud-based validation model, is explained, highlighting how it reduces false rejects while manages false accepts. This talk concludes by discussing the effectiveness of this approach and its successful application in Roku's Voice Remote Pro. A Q&A session follows for further discussion.

Author:

Frank Maker

Director, Software - Remotes, Voice and EdgeML
Roku

Frank Maker is Director of Software at Roku - his team is responsible for Voice, EdgeML, and Remote software He is the engineering owner for remotes and develops innovative EdgeML models for Roku´s new products.

In his role, Frank is responsible for:

* Embedded machine learning (TinyML / EdgeML)
* Microcontroller firmware development
* Embedded Linux firmware development (RokuOS)
* Embedded machine learning model development and deployment
* Automated EdgeML testing
* Wi-Fi remote development

 

... read more

Frank Maker

Director, Software - Remotes, Voice and EdgeML
Roku

Frank Maker is Director of Software at Roku - his team is responsible for Voice, EdgeML, and Remote software He is the engineering owner for remotes and develops innovative EdgeML models for Roku´s new products.

In his role, Frank is responsible for:

* Embedded machine learning (TinyML / EdgeML)
* Microcontroller firmware development
* Embedded Linux firmware development (RokuOS)
* Embedded machine learning model development and deployment
* Automated EdgeML testing
* Wi-Fi remote development

 

 

Jaya Kawale

VP of Engineering, AI/ML
Tubi

Jaya Kawale is the head of Machine Learning at Tubi, a Fox Corporation content platform. Jaya´s team works on solving various ML problems for Tubi´s product, ranging from recommendations, content understanding and acquisition, ads ML, etc. Her team also work on the application of cutting edge machine learning technologies such as contextual bandits, deep learning, computer vision and NLP to improve user experience at Tubi.

Read full bio

Jaya Kawale

VP of Engineering, AI/ML
Tubi

Jaya Kawale

VP of Engineering, AI/ML
Tubi

Jaya Kawale is the head of Machine Learning at Tubi, a Fox Corporation content platform. Jaya´s team works on solving various ML problems for Tubi´s product, ranging from recommendations, content understanding and acquisition, ads ML, etc. Her team also work on the application of cutting edge machine learning technologies such as contextual bandits, deep learning, computer vision and NLP to improve user experience at Tubi.

2023 Post-Show Report
 

Soojung Ryu

CEO
SAPEON

As a well-known expert in AI processors, Soojung Ryu is in charge of SAPEON in order to accelerate the company’s growth in the global AI market. She brings more than 25 years of extensive experience in leading various projects related to NPU and GPU.

Read full bio

Soojung Ryu

CEO
SAPEON

Soojung Ryu

CEO
SAPEON

As a well-known expert in AI processors, Soojung Ryu is in charge of SAPEON in order to accelerate the company’s growth in the global AI market. She brings more than 25 years of extensive experience in leading various projects related to NPU and GPU.

Before she joined SK Telecom as the head of the AI accelerator office, Ryu was a University-Industry Collaboration Professor at Seoul National University, where she conducted R&D in the NPU and PIM. When she served as the Vice President of Samsung Group's R&D hub, she undertook diverse projects related to GPU. Ryu received her Ph.D. degree in Electrical & Computer Engineering from Georgia Institute of Technology.

Author:

Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.

... read more

Animesh Singh

Executive Director, AI & Machine Learning
LinkedIn

Executive Director, AI and ML Platform at LinkedIn | Ex IBM Senior Director and Distinguished Engineer, Watson AI and Data | Founder at Kubeflow | Ex LFAI Trusted AI NA Chair

Animesh is the Executive Director leading the next generation AI and ML Platform at LinkedIn, enabling creation of AI Foundation Models Platform, serving the needs of 930+ Million members of LinkedIn. Building Distributed Training Platform, Machine Learning Pipelines, Feature Pipelines, Metadata engine etc. Leading the creation of LinkedIn GAI platform for fine tuning, experimentation and inference needs. Animesh has more than 20 patents, and 50+ publications. 

Past IBM Watson AI and Data Open Tech CTO, Senior Director and Distinguished Engineer, with 20+ years experience in Software industry, and 15+ years in AI, Data and Cloud Platform. Led globally dispersed teams, managed globally distributed projects, and served as a trusted adviser to Fortune 500 firms. Played a leadership role in creating, designing and implementing Data and AI engines for AI and ML platforms, led Trusted AI efforts, drove the strategy and execution for Kubeflow, OpenDataHub and execution in products like Watson OpenScale and Watson Machines Learning.