Inference Acceleration: Follow The Memory


Much has been written about the computational complexity of inference acceleration: very large matrix multiplies for fully-connected layers and huge numbers of 3x3 convolutions across megapixel images, both of which require many thousands of MACs (multiplier-accumulators) to achieve high throughput for models like ResNet-50 and YOLOv3. The other side of the coin is managing the movement of d... » read more

In-Memory Vs. Near-Memory Computing


New memory-centric chip technologies are emerging that promise to solve the bandwidth bottleneck issues in today’s systems. The idea behind these technologies is to bring the memory closer to the processing tasks to speed up the system. This concept isn’t new and the previous versions of the technology fell short. Moreover, it’s unclear if the new approaches will live up to their billi... » read more

Use Inference Benchmarks Similar To Your Application


If an Inference IP supplier or Inference Accelerator Chip supplier offers a benchmark, it is probably ResNet-50. As a result, it might seem logical to use ResNet-50 to compare inference offerings. If you plan to use ResNet-50 it would be; but if your target application model is significantly different from Resnet-50 it could lead you to pick an inference offering that is not best for you. ... » read more

In-Memory Computing Challenges Come Into Focus


For the last several decades, gains in computing performance have come by processing larger volumes of data more quickly and with superior precision. Memory and storage space are measured in gigabytes and terabytes now, not kilobytes and megabytes. Processors operate on 64-bit rather than 8-bit chunks of data. And yet the semiconductor industry’s ability to create and collect high quality ... » read more

Power/Performance Bits: Jan. 29


Neural nets struggle with shape Cognitive psychologists at the University of California Los Angeles investigated how deep convolutional neural networks identify objects and found a big difference between the way these networks and humans perceive objects. In the first of a series of experiments, the researchers showed color images of animals and objects that had been altered to have a diffe... » read more

What’s Next For AI, Quantum Chips


Semiconductor Engineering sat down to discuss the latest R&D trends with Luc Van den hove, president and chief executive of Imec; Emmanuel Sabonnadière, chief executive of Leti; and An Chen, executive director for the Nanoelectronics Research Initiative at the Semiconductor Research Corp. (SRC). Chen is on assignment from IBM. What follows are excerpts of those conversations, which took pl... » read more

Power/Performance Bits: Jan. 22


Efficient neural net training Researchers from the University of California San Diego and Adesto Technologies teamed up to improve neural network training efficiency with new hardware and algorithms that allow computation to be performed in memory. The team used an energy-efficient spiking neural network for implementing unsupervised learning in hardware. Spiking neural networks more closel... » read more

Hardware Mathematics for Artificial Intelligence


Article written by John A. Swanson, Sr. Product Marketing Manager, Synopsys Artificial intelligence (AI) has the potential to fundamentally change the way we interact with our devices and live our lives. Petabytes of data efficiently travels between edge devices and data centers for processing and computing of AI tasks. The ability to process real world data and create mathematical represen... » read more

AI In Chip Manufacturing


Ira Leventhal, New Concept Product Initiative vice president at Advantest, talks with Semiconductor Engineering about using analysis and deep learning to make test more efficient and more effective. https://youtu.be/3VVG4JVnjHo » read more

Edge Inferencing Challenges


Geoff Tate, CEO of Flex Logix, talks about balancing different variables to improve performance and reduce power at the lowest cost possible in order to do inferencing in edge devices. https://youtu.be/1BTxwew--5U » read more

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