“I brought this up because we began thinking about using this for place-and-route in ASIC design,” Dean said. “The game of place-and-route is far bigger than the game of go. The problem size is larger, though there isn’t as clear goal as there is with go.”
Google created a learning model for place-and-route, and then set out to find if the tool could generalize. Could it take what it learned on one design and apply it to a new design it had never seen before? The answer was an unambiguous “yes.”
Furthermore, Dean said, “We’ve gotten super-human results on all the blocks we’ve tried so far. It does a little bit better, and sometimes significantly better than humans.”
Machine Learning
EETimes – Seeing the AI Inference Market with 2020 Vision: Our Top 5 Predictions –
So AI chips optimized for inference, not for graphics, training or DSP, will be the big thing in 2020. Here is one perspective on the top 5 predictions for AI inference.
Source: EETimes – Seeing the AI Inference Market with 2020 Vision: Our Top 5 Predictions –
Is Intel Considering Another AI Acquisition? – EETimes
Rumours abound that Habana Labs may be in discussion with Intel. here would a potential Habana Labs acquisition leave Nervana? There is a possibility that Intel could decide to sideline the NNP-T and NNP-I product families in favour of Habana’s offering.
Source: Is Intel Considering Another AI Acquisition? – EETimes
Introducing TensorBoard.dev: a new way to share your ML experiment results
Posted by Gal Oshri , Product Manager TensorBoard , TensorFlow’s visualization toolkit, is often used by researchers and engineers to visualize and understand their ML experiments. It enables tracking experiment metrics , visualizing models , profiling ML programs , visualizing hyperparameter tuning experiments , and much more.
We have seen people sharing screenshots of their TensorBoards to achieve this. However, screenshots aren’t interactive and fail to capture all the details. At Google, researchers and engineers often communicate their insights about model behavior by sending their TensorBoard visualizations to teammates. Our goal is to provide this capability to the broader community.
Source: Introducing TensorBoard.dev: a new way to share your ML experiment results
2019 Intel AI Summit (Replay)
» Download “2019 Intel AI Summit (Event Replay)” Intel hosted its 2019 Intel AI Summit on Tuesday, Nov. 12, 2019, in San Francisco. Watch the replay to hear from Naveen Rao, Intel corporate vice president and general manager of the Intel Artificial Intelligence Products Group, as he shares significant product updates across the Intel artificial intelligence portfolio in addition to Intel’s vision for the future of AI hardware and software.
Source: 2019 Intel AI Summit (Replay)
Intel Unveils New GPU Architecture with High-Performance Computing and AI Acceleration, and oneAPI Software Stack with Unified and Scalable Abstraction for Heterogeneous Architectures
Intel launches oneAPI, a unified and scalable programming model to harness the power of diverse computing architectures in the era of
Growing Pains: Scaling Deep Learning Inference
Training an effective deep neural network is one thing, but deploying it in a way that keeps up with customer demand and is both performant and cost-efficient is hard. We’ve combined a heavily optimized software stack with deep learning-enabled hardware to fix that.