A Packet-Based Architecture For Edge AI Inference

Despite significant improvements in throughput, edge AI accelerators (Neural Processing Units, or NPUs) are still often underutilized. Inefficient management of weights and activations leads to fewer available cores utilized for multiply-accumulate (MAC) operations. Edge AI applications frequently need to run on small, low-power devices, limiting the area and power allocated for memory and comp... » read more

Low Density Of LPDDR4x DRAM — The Best Choice For Edge AI

Edge AI computes the data as close as possible to the physical system. The advantage is that the processing of data does not require a connected network. The computation of data happens near the edge of a network, where the data is being developed, instead of in a centralized data-processing center. One of the biggest benefits of edge AI is the ability to secure real-time results for time-sensi... » read more

Review of Tools & Techniques for DL Edge Inference

A new technical paper titled "Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review" was published in "Proceedings of the IEEE" by researchers at University of Missouri and Texas Tech University. Abstract: Successful integration of deep neural networks (DNNs) or deep learning (DL) has resulted in breakthroughs in many areas. However, deploying thes... » read more

Edge-AI Hardware for Extended Reality

New technical paper titled "Memory-Oriented Design-Space Exploration of Edge-AI Hardware for XR Applications" from researchers at Indian Institute of Technology Delhi and Reality Labs Research, Meta. Abstract "Low-Power Edge-AI capabilities are essential for on-device extended reality (XR) applications to support the vision of Metaverse. In this work, we investigate two representative XR w... » read more

MIPI In Next Generation Of AI IoT Devices At The Edge

The history of data processing begins in the 1960’s with centralized on-site mainframes that later evolved into distributed client servers. In the beginning of this century, centralized cloud computing became attractive and began to gain momentum becoming one of the most popular computing tools today. In recent years however, we have seen an increase in the demand for processing... » read more

Fast and Flexible FPGA-based NoC Hybrid Emulation

Researchers from RWTH Aachen University and Otto-von-Guericke Universitat Magdeburg have published a new technical paper titled "EmuNoC: Hybrid Emulation for Fast and Flexible Network-on-Chip Prototyping on FPGAs." Abstract: "Networks-on-Chips (NoCs) recently became widely used, from multi-core CPUs to edge-AI accelerators. Emulation on FPGAs promises to accelerate their RTL modeling co... » read more

AI At The Edge: Optimizing AI Algorithms Without Sacrificing Accuracy

The ultimate measure of success for AI will be how much it increases productivity in our daily lives. However, the industry has huge challenges in evaluating progress. The vast number of AI applications is in constant churn: finding the right algorithm, optimizing the algorithm, and finding the right tools. In addition, complex hardware engineering is rapidly being updated with many different s... » read more

Mapping Transformation Enabled High-Performance and Low-Energy Memristor-Based DNNs

Abstract: "When deep neural network (DNN) is extensively utilized for edge AI (Artificial Intelligence), for example, the Internet of things (IoT) and autonomous vehicles, it makes CMOS (Complementary Metal Oxide Semiconductor)-based conventional computers suffer from overly large computing loads. Memristor-based devices are emerging as an option to conduct computing in memory for DNNs to make... » read more

Seven Hardware Advances We Need to Enable The AI Revolution

The potential, positive impact AI will have on society at large is impossible to overestimate. Pervasive AI, however, remains a challenge. Training algorithms can take inordinate amounts of power, time, and computing capacity. Inference will also become more taxing with applications such as medical imaging and robotics. Applied Materials estimates that AI could consume up to 25% of global elect... » read more

Flexible USB4-Based Interface IP Solution For AI At The Edge

Consumers have become accustomed to smart devices that are powered by advances in artificial intelligence (AI). To expand the devices’ total addressable market, innovative device designers build edge AI accelerators and edge AI SoCs that support multiple use cases and integration options. This white paper describes a flexible USB4-based IP solution for edge AI accelerators and SoCs. The IP so... » read more

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