A List of Chip/IP for Deep Learning (keep updating)
1. AI artist conjures up convincing fake worlds from memories
Take a look at the above image of a German street. At a glance it could be a blurry dashcam photo, or a snap that’s gone through one of those apps that turns photos into paintings. But you won’t find this street anywhere on Google Maps. That’s because it was generated by an imaginative neural network, stitching together its memories of real streets it was trained on.
2. Create Anime Characters with A.I. !
We all love anime characters and are tempted to create our own, but most of us cannot do that because we are not professional artists. What if anime characters could be generated automatically at a professional level of quality? Imagine that you could just specify attributes (such as blonde/twin tailed/smiling), and have an anime character with your customizations generated without any further intervention!
Just try it!
3. TVM: An End to End IR Stack for Deploying the Deep Learning Workloads to Hardwares
We are excited to announce the launch of TVM as solution to this problem. TVM is a novel framework that can:
- Represent and optimize the common deep learning computation workloads for CPUs, GPUs and other specialized hardware
- Automatically transform the computation graph to minimize memory utilization, optimize data layout and fuse computation patterns
- Provide an end-to-end compilation from existing front-end frameworks down to bare-metal hardware, all the way up to browser executable javascripts.
4. AI, Native Supercomputing and The Revival of Moore's Law
Based on Alan Turing's proposition on AI and computing machinery, which shaped Computing as we know it today, the new AI computing machinery should comprise a universal computer and a universal learning machine. The later should understand linear algebra natively to overcome the slowdown of Moore's law. In such a universal learnig machine, a computing unit does not need to keep the legacy of a universal computing core. The data can be distributed to the computing units, and the results can be collected from them through Collective Streaming, reminiscent of Collective Communication in Supercomputing. It is not necessary to use a GPU-like deep memory hierarchy, nor a TPU-like fine-grain mesh.
5. We are making on-device AI ubiquitous
We envision a world where devices, machines, automobiles, and things are much more intelligent, simplifying and enriching our daily lives. They will be able to perceive, reason, and take intuitive actions based on awareness of the situation, improving just about any experience and solving problems that to this point we’ve either left to the user, or to more conventional algorithms.
6. Why Elon Musk is Wrong about AI
Even if we wanted to stop super-intelligent machines from slaughtering us all, we can’t. Why? Because they don’t exist and you can’t create a solution for a problem that doesn’t exist. So let’s focus on issues that really matter today instead of ones that won’t matter for 50 or 100 years. Or else we won’t need Terminators to wipe us out.
7. Machine Learning and Deep Learning Mindmap and Cheatsheet
A Mindmap summarising Machine Learning concepts, from Data Analysis to Deep Learning.
Very good introduction of three waves of AI: Handcraft Knowledge, Statistical Learning and Contextual Adaptation, with a short video and slides.