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Chris Kachris
  • Male
  • Wilmington, DE
  • United States
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Chris Kachris's blog post was featured

Open-source Logistic Regression FPGA core for accelerated Machine Learning

Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized architectures for the efficient…See More
Jul 3
Chris Kachris posted a blog post

How to make ML engineers 5x more efficient

Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.Salaries and OpExAt the same time…See More
May 19
Chris Kachris posted a blog post

How to save over $200k on your next machine learning project

Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.Typical machine learning application (especially when auto ML is used) can scale to as much as 30 nodes or higher (servers) to provide useful…See More
May 16
Thomas Loock commented on Chris Kachris's blog post Speedup your Machine Learning applications without changing your code
"The MNIST dataset is less than 20 Megabytes and not 24 GBytes. MNIST Home About what dataset are you talking?"
Feb 24
Tauheedul Ali liked Chris Kachris's blog post Speedup your Machine Learning applications without changing your code
Jan 4
Tauheedul Ali liked Chris Kachris's blog post Speedup your Machine Learning applications without changing your code
Jan 4
Prabhakaran Sampath liked Chris Kachris's blog post Speedup your Machine Learning applications without changing your code
Dec 10, 2018
Chris Kachris posted a blog post

Speedup your Machine Learning applications without changing your code

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack of programming efficiency as they are programmed…See More
Dec 5, 2018
Chris Kachris liked Chris Kachris's blog post Speedup your Machine Learning applications without changing your code
Nov 16, 2018
Chris Kachris shared their blog post on Twitter
Nov 16, 2018
Chris Kachris posted a blog post

Speedup your Machine Learning applications without changing your code

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack of programming efficiency as they are programmed…See More
Nov 15, 2018
Chris Kachris's blog post was featured

Speedup your Machine Learning applications without changing your code

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack of programming efficiency as they are programmed…See More
Nov 15, 2018

Profile Information

Short Bio
Chris Kachris is the CEO and co-founder of InAccel, Inc. InAccel helps companies speedup their ML applications on the cloud using FPGA-based accelerators.
My Web Site Or LinkedIn Profile
http://www.linkedin.com/in/kachris/
Field of Expertise
Data Science, Machine Learning
Professional Status
C-Level
Years of Experience:
10
Your Company:
InAccel
Industry:
Machine learning
Your Job Title:
CEO
Interests:
Contributing, Networking, New venture

Chris Kachris's Blog

Open-source Logistic Regression FPGA core for accelerated Machine Learning

Posted on July 1, 2019 at 10:27pm 0 Comments

Machine learning algorithms are extremely computationally intensive and time consuming when they must be trained on large amounts of data. Typical processors are not optimized for machine learning applications and therefore offer limited performance. Therefore, both academia an industry is focused on the development of specialized…

Continue

How to make ML engineers 5x more efficient

Posted on May 17, 2019 at 4:50am 0 Comments

Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013.

Salaries and…

Continue

How to save over $200k on your next machine learning project

Posted on May 14, 2019 at 11:30pm 0 Comments

Machine learning applications require powerful and scalable computing systems that can sustain the high computation complexity of these applications. Companies that are working on the domain of machine learning have to allocate a significant amount of their budget for the OpEx of machine learning applications whether this is done on cloud or on-prem.

Typical machine learning application…

Continue

Speedup your Machine Learning applications without changing your code

Posted on November 6, 2018 at 7:00am 2 Comments

Emerging cloud applications like machine learning, AI and big data analytics require high performance computing systems that can sustain the increased amount of data processing without consuming excessive power. Towards this end, many cloud operators have started adopting heterogeneous infrastructures deploying hardware accelerators, like FPGAs, to increase the performance of computational intensive tasks. However, most hardware accelerators lack…

Continue

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