High Neural Inferencing Throughput At Batch=1


Microsoft presented the following slide as part of their Brainwave presentation at Hot Chips this summer: In existing inferencing solutions, high throughput (and high % utilization of the hardware) is possible for large batch sizes: this means that instead of processing say one image at a time, the inferencing engine processes say 10 or 50 images in parallel. This minimizes the number of... » read more

Inferencing In Hardware


Cheng Wang, senior vice president of engineering at Flex Logix, examines shifting neural network models, how many multiply-accumulates are needed for different applications, and why programmable neural inferencing will be required for years to come. https://youtu.be/jb7qYU2nhoo         See other tech talk videos here. » read more

Power/Performance Bits: Nov. 20


In-memory compute accelerator Engineers at Princeton University built a programmable chip that features an in-memory computing accelerator. Targeted at deep learning inferencing, the chip aims to reduce the bottleneck between memory and compute in traditional architectures. The team's key to performing compute in memory was using capacitors rather than transistors. The capacitors were paire... » read more

The Week In Review: Design


M&A GlobalFoundries formed Avera Semiconductor, a wholly-owned subsidiary focused on custom ASIC designs. While Avera will use its relationship with GF for 14/12nm and more mature technologies, it has a foundry partnership lined up for 7nm. The new company's IP portfolio includes high-speed SerDes, high-performance embedded TCAMs, ARM cores and performance and density-optimized embedded SR... » read more

Real-Time Object Recognition At Low Cost/Power/Latency


Most neural network chips and IP talk about ResNet-50 benchmarks (image classification at 224x224 pixels). But we find that the number one neural network of interest for most customers is real-time object recognition, such as YOLOv3. It's not possible to do comparisons here because nobody shows a YOLOv3 benchmark for their inferencing. But it's very possible to improve on the inferencing per... » read more

Making AI Run Faster


The semiconductor industry has woken up to the fact that heterogeneous computing is the way forward and that inferencing will require more than a GPU or a CPU. The numbers being bandied about by the 30 or so companies working on this problem are 100X improvements in performance. But how to get there isn't so simple. It requires four major changes, as well as some other architectural shifts. ... » read more

Using ASICs For AI Inferencing


Flex Logix’s Cheng Wang looks at why ASICs are the best way to improve performance and optimize power and area for inferencing, and how to add flexibility into those designs to deal with constantly changing algorithms and data sets. https://youtu.be/XMHr7sz9JWQ » read more

The Next Big Chip Companies


Rambus’ Mike Noonen looks at why putting everything on a single die no longer works, what comes after Moore’s Law, and what the new business model looks like for chipmakers. https://youtu.be/X6Kca8Vm-wA » read more

Intel’s Next Move


Gadi Singer, vice president and general manager of Intel's Artificial Intelligence Products Group, sat down with Semiconductor Engineering to talk about Intel's vision for deep learning and why the company is looking well beyond the x86 architecture and one-chip solutions. SE: What's changing on the processor side? Singer: The biggest change is the addition of deep learning and neural ne... » read more

Scalable, Cloud-Ready IC Validator Solution For Advanced DRC Nodes


As we move to a data-centric world, semiconductor companies across the globe are working at a furious pace to develop and manufacture Artificial Intelligent [AI] chips. AI is all about an algorithm that mimics a human’s ability to learn and decide. For example, AI can be used to interpret and understand an image that helps a doctor make a better diagnosis for a patient. This requires chips to... » read more

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