Efinix
Semiconductors, 900 Lafayette St, Cupertino, California, 95050, United States, 51-200 Employees
Phone Number: 40********
Who is EFINIX
Our mission is to drive the future of edge computing with our FPGA silicon platforms. At the heart of our FPGAs is our disruptive QuantumTM fabric, which delivers a big Power-Performance-...
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Headquarters: 900 Lafayette St, Cupertino, California, 95050, United States
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Date Founded: 2012
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Employees: 51-200
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Revenue: $25 Million to $50 Million
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Active Tech Stack: See technologies
Industry: Semiconductors
SIC Code: 3674
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NAICS Code: 334413 |
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Similar Companies to Efinix
Lattice Semiconductor
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1001-5000
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Frequently Asked Questions Regarding Efinix
Answer: Efinix's headquarters are located at 900 Lafayette St, Cupertino, California, 95050, United States
Answer: Efinix's phone number is 40********
Answer: Efinix's official website is https://efinixinc.com
Answer: Efinix's revenue is $25 Million to $50 Million
Answer: Efinix's SIC: 3674
Answer: Efinix's NAICS: 334413
Answer: Efinix has 51-200 employees
Answer: Efinix is in Semiconductors
Answer: Efinix top competitors include: Lattice Semiconductor
Answer: Efinix contact info: Phone number: 40******** Website: https://efinixinc.com
Answer: Our mission is to drive the future of edge computing with our FPGA silicon platforms. At the heart of our FPGAs is our disruptive QuantumTM fabric, which delivers a big Power-Performance-Area advantage over traditional FPGA technologies. Trion FPGAs, offering 4K to 120K logic elements (LEs), have a small form factor, low power, are priced for high-volume production, and are available now in volume quantities. Taking it to the next level, Trion Titanium FPGAs have an enhanced Quantum compute fabric combined with a 16 nm process to give even lower power and higher speeds. With densities ranging from 25K LEs to 500K LEs, Titanium FPGAs are ready to meet your next design challenge. With their Power-Performance-Area advantage, Trion and Trion Titanium FPGAs address applications such as custom logic, compute acceleration, machine learning, and deep learning.
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