Author Archives: kunjal

Network Services in a Multi-Vendor Environment

Monday, July 15, 2019, 6:30 PM – 9:00 PM PDT

The event is FREE. Food & drinks will be provided.

This event is organized by:

 Co-sponsors:

 Speaker1: Saikrishna Kotha
                       Director, Global Network Engineering

Speaker2: Anand Palaniswamy
                       Sr. Manager, Cloud Engineering at PayPal

Location: PayPal Town Hall, 2161 N 1st St. San Jose, CA 95131  View Map  

(Register here)

PROGRAM:

  • 6:30 – 7:00 PM Registration, Pizza, Networking & Refreshments
  • 7:00 – 8:30PM Technical Talks
  • 8:30 – 8:50PM Q&A/Adjourn
  • 9:00PM everybody must vacate the building 

1) Speaker1:

Abstract:
Core Network Services provides network connectivity for PayPal site infrastructure. To address 5 9s’ site reliability goals, the demand for network to perform at its best keeps growing. PayPal transformed its data center networks by deploying white-box leaf/spine designs to help with horizontal scaling, low latency, and resilience to failures. By developing common framework layer, PayPal is able to address network automation/service API needs in multi-vendor environment. Saikrishna will discuss the design approach, lessons learned and how various vendor technologies are leveraged in this transformation process.

Bio: Saikrishna Kotha

Saikrishna has 20 years of technology leadership and management experience in networking domain spanning across optical, wireless, and wired technologies. Saikrishna’s recent focus has been implementing web scale Datacenter networking technologies. Currently he leads PayPal’s Core Network Services organization. Prior to PayPal, Saikrishna worked at LinkedIn where he was instrumental in building white-box network switches. He also worked at Dell, Xilinx, Tasman Networks (acquired by Nortel) and Ciena. Saikrishna holds 12 issued patents and 15 pending in converged storage/networking technologies, SDN and NFV domains. Saikrishna holds MBA from TAMU-commerce and Bachelor’s degree in ECE from Andhra University, India.
 LinkedIn profile: https://www.linkedin.com/m/profile/saikrishnakotha/

2) Speaker2:

Abstract:
Core Network Services provides network connectivity for PayPal site infrastructure. To address 5 9s’ site reliability goals, the demand for network to perform at its best keeps growing. PayPal transformed its data center networks by deploying white-box leaf/spine designs to help with horizontal scaling, low latency, and resilience to failures. By developing common framework layer, PayPal is able to address network automation/service API needs in multi-vendor environment. Saikrishna will discuss the design approach, lessons learned and how various vendor technologies are leveraged in this transformation process.

Bio: Anand Palaniswamy

Anand has 18+ years of experience and works at PayPal for last 7 years. Currently, he is focused on building APIs to enable self-service capabilities for PayPal Core Network Services and he was leading Cloud Software Engineering team for building PayPal private cloud. Prior to PayPal, he was part of eBay for an year and worked for Tellabs where he was building frameworks and platform for Network Management System. Anand received his Master’s degree in Computer Science from University of Madras.

Open to all to attend

(Online registration is needed. If you did not register, seating is not guaranteed.)

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Squeezing down the computing requirements of deep neural networks

Wednesday, July 24, 2019, 6:00 PM – 8:00 PM PDT  

The event is FREE. Food & drinks will be provided.

This event is organized by:

 Co-sponsors:

Speaker:  Forrest Iandola, CEO and co-Founder of DeepScale

Location: 673 South Milpitas Blvd. Milpitas, CA 95035, USA  View Map 

(Register here)

PROGRAM:

  • 6:00 – 6:30 PM Networking & Refreshments
  •  6:30 – 7:30 PM Talk
  • 7:30 – 8:00 PM Q&A/Adjourn

Abstract: Deep Neural Networks (DNNs) have enabled breakthrough levels of accuracy on a variety of tasks in vision, audio, and text. However, DNNs can be quite computationally-intensive, and highly-accurate DNNs often require a full-sized GPU server for real-time inference. To squeeze DNNs into smaller computing footprints, there are a number of techniques, including better DNN design, DNN quantization, better implementations of DNNs, and better utilization of specialized computing hardware. This talk touches on all these techniques, with particular focus on better DNN design for computer vision. Recently, Neural Architecture Search (NAS) technologies have begun to make significant progress in automating the process of designing “squeezed” DNNs, and we cover some of the latest work on NAS in this talk.

Bio: Forrest Iandola  

Forrest Iandola completed a PhD in Electrical Engineering and Computer Science at UC Berkeley, where his research focused on improving the efficiency of deep neural networks (DNNs). His best-known published research ranges from scaling DNN training to hundreds of GPUs (FireCaffe), to squeezing DNNs onto small edge-devices (SqueezeNet and SqueezeDet). His advances in scalable training and efficient inference of DNNs led to the founding of DeepScale, where he has been CEO since 2015. DeepScale builds energy-efficient vision and perception systems for automated vehicles.

Open to all to attend

(Online registration is needed. If you did not register, seating is not guaranteed.)

Eventbrite - enabling-digital-transformation-at-the-edge

 

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