Hailo is an Israel-based deep learning company that develops a breakthrough specialized deep learning microprocessor to deliver data center performance to edge devices. The company aims to drive artificial intelligence to the edge. Imagine intelligent devices that are empowered with the performance of a data center class computer, operating in real-time at reduced power consumption, size, and cost. Hailo processors enable edge devices to go beyond handling sensors and streaming volumes of data for remote processing–they can do the processing themselves. Hailo’s complete re-think of computer architecture breaks the barriers of traditional paradigms, accelerating the extremely heavy computation of deep neural networks.
The industry-leading Hailo-8 edge AI processor features up to 26 tera-operations per second (TOPS). One small, low-power, and self-contained chip can process high-resolution video in real-time or multiple video streams and neural network models simultaneously, all within a small power budget and rigid heat dissipation constraints. The Hailo-8 AI accelerator is commercially available. We offer a range of standard form-factor modules and an on-PCB design track, as well as a robust and mature software toolchain.
Based in Tel-Aviv with offices in the US, Germany, Japan, and Taiwan, we are growing and expanding our worldwide partner ecosystem. Our novel, patented dataflow architecture is finding various applications across industries, including Automotive, Smart City, Industry 4.0, Smart Retail, and more. Hailo’s holistic solution redefines the traditional pillars of embedded computer architecture; memory, control, computing, and the relations between them. By designing an AI hardware architecture that relies purely on the core properties of neural networks, edge devices can now run deep learning applications at full scale more efficiently, effectively, and sustainably than traditional solutions.
Total Funding: $340M
Funding Stage: Series C
Business Stage: Scaling Up
Market: B2B
Company Size: 101 to 250
Founded: 2017
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