Welcome to Deeplite's blog! A place to discover helpful content on computer vision and perception model optimization, inference acceleration, tinyML and other topics related to achieving smaller, faster and more cost-efficient AI to run on edge devices and in the cloud.
Exciting news! We are thrilled to unveil the latest hardware benchmarks for the cutting-edge Raspberry Pi 5 featuring the Arm Cortex A76 processor @ 2.4GHz. Dive into the comparison between the Raspberry Pi 5 and 4 models, as well as other edge hardware platforms, using the powerful YOLOBench tool.
This blog is a summary of a Medium article that you can find here.
We are excited to release YOLOBench, a latency-accuracy benchmark of over 900 YOLO-based object detectors for embedded use cases. Accepted at the ICCV 2023 RCV workshop, you can read the full paper on arXiv.
Check out the...
The proliferation of edge devices has unlocked unprecedented opportunities for deep learning model deployment in computer vision applications. However, these complex models require considerable power, memory and compute resources that are typically not available on edge platforms. Ultra low-bit...
Recently, Deeplite CEO Nick Romano sat down with CyberNews, a resource for cybersecurity news, tech analysis and product reviews, to discuss Deeplite, the AI industry and what he thinks is coming for the future of AI.
We were pleased to announce earlier this week that Deeplite was crowned as a Silver Stevie winner in the 2022 International Business Awards, in the Artificial Intelligence/Machine Learning Solution category. The team was thrilled to receive this honor and get recognition for Deeplite Neutrino and...
Deeplite’s AI optimization solutions honored with Silver Stevie® award
Here at Deeplite, we've been enjoying a number of opportunities to safely talk about the work we are doing, in person! Through a number of recent industry trade shows, we've been able to promote our solutions and how we are making AI accessible across a wide range of edge devices.