
Deci AI
The deep learning acceleration platform for developers to build, optimize, and deploy ultra-fast models on any hardware.
- Active
- Software
- Series B
News
Business Overview
Business Description
Deci enables deep learning to live up to its true potential by using AI to build better AI. With the company’s end-to-end deep learning acceleration platform, AI developers can build, optimize, and deploy faster and more accurate models for any environment, including cloud, edge, or mobile. With Deci’s platform, developers can increase deep learning model inference performance by 3x-15x, on any hardware, while still preserving accuracy. This translates directly into new use cases on limited hardware, substantially shorter development cycles, and reduced compute costs by up to 80%. The platform is powered by Deci’s Automated Neural Architecture Construction (AutoNAC) technology, an algorithmic optimization engine that squeezes maximum utilization out of any hardware. The AutoNAC engine contains a Neural Architecture Search (NAS) component that redesigns a given trained model’s architecture to optimally improve its inference performance (throughput, latency, memory, etc.) for specific target hardware while preserving its baseline accuracy. Deci achieved a record-breaking 11.8x accelerated inference speedup on Intel CPUs at MLPerf Industry Benchmark and has been named to the CBInsights top 100 AI companies. Led by a team of world-class deep learning experts, Deci lets AI developers focus on what they do best – creating innovative AI-based solutions for our world’s most complex problems.
Operating Status
Active
Founded
October 2019
Total Employees
77
Sectors
Sub Sectors
Offering Type
Software
Business Model
B2B
Business Stage
Launched
People
Funding
Total Funding
$55,100,000
Last Funding Round
Series A
Valuation
Funding Rounds
Investors
AI Technology Stack
AI Description
Deci’s deep learning platform is powered by Deci’s Automated Neural Architecture Construction (AutoNAC) technology, an algorithmic optimization engine that squeezes maximum utilization out of any hardware. The AutoNAC engine contains a Neural Architecture Search (NAS) component that redesigns a given trained model’s architecture to optimally improve its inference performance (throughput, latency, memory, etc.) for specific target hardware while preserving its baseline accuracy.
AI Technology
Deep Learning
AI Employees
14
Learning Types
AI Tasks
Object Detection, Recommendation System, Segmentation
Algorithms and Techniques
Classification, Deep Neural Networks (DNN), Neural Networks
Cloud Provider
Frameworks, Libraries and Tools
Pytorch, TensorFlow
Coding Languages
Python