Deployable Artificial Neural Networks Will Change Everything
In recent months, evidence has continued to mount that artificial neural networks of the "deep learning" variety are significantly better than previous techniques at a diverse range of visual understanding tasks.
For example, Yannis Assael and colleagues from Oxford have demonstrated a deep learning algorithm for lip reading that is dramatically more accurate than trained human lip readers, and much more accurate than the best previously published algorithms.
Meanwhile, Andre Esteva, Brett Kuprel and colleagues at Stanford described a deep learning algorithm for diagnosing skin cancer that is as accurate as typical dermatologists (who, in the U.S., complete 12 years of post-secondary education before they begin practicing independently).
Even for tasks where classical computer vision algorithms have been successful, deep learning is raising the bar. Examples include optical flow (estimating motion in a sequence of video frames) and stereo matching (matching features in images captured by a pair of stereo cameras).
To read the full article, click here
Related Semiconductor IP
- Process/Voltage/Temperature Sensor with Self-calibration (Supply voltage 1.2V) - TSMC 3nm N3P
- USB 20Gbps Device Controller
- SM4 Cipher Engine
- Ultra-High-Speed Time-Interleaved 7-bit 64GSPS ADC on 3nm
- Fault Tolerant DDR2/DDR3/DDR4 Memory controller
Related Blogs
- Embedded Vision: The Road Ahead for Neural Networks and Five Likely Surprises
- Push-button generation of deep neural networks
- Hierarchical Neural Networks
- Neural Networks and the Future
Latest Blogs
- Shaping the Future of Semiconductor Design Through Collaboration: Synopsys Wins Multiple TSMC OIP Partner of the Year Awards
- Pushing the Boundaries of Memory: What’s New with Weebit and AI
- Root of Trust: A Security Essential for Cyber Defense
- Evolution of AMBA AXI Protocol: An Introduction to the Issue L Update
- An Introduction to AMBA CHI Chip-to-Chip (C2C) Protocol