Survey of Energy Efficient PIM Processors


A new technical paper titled "Survey of Deep Learning Accelerators for Edge and Emerging Computing" was published by researchers at University of Dayton and the Air Force Research Laboratory. Abstract "The unprecedented progress in artificial intelligence (AI), particularly in deep learning algorithms with ubiquitous internet connected smart devices, has created a high demand for AI compu... » read more

Simplifying AI Deployment from the Cloud to Edge and Endpoint


Artificial Intelligence (AI) is transforming every aspect of life. It is enhancing quality in industrial applications, enabling smart home systems, monitoring our safety as we work and play. Advances in technology have allowed us to run complex machine learning algorithms to tackle unique problems allowing those to be implemented also on embedded devices used in our daily life in home and indus... » read more

On-Device Speaker Identification For Digital Television (DTV)


In recent years, the way we interact with our TVs has changed. Multiple button presses to navigate an on-screen keyboard have been replaced with direct interaction through our voices. While this has resulted in significant improvements to the Digital Television (DTV) user experience, more can be done to provide immersive and engaging experiences. Imagine you say, “recommend me a film” or... » read more

Evolving Edge Computing


Edge computing is a term that has been in use for a long time. Throughout the industry, there are many references to edge and many pre-conceptions about what that might mean. The term ‘edge’ is typically used for devices that exist on the edge of a network and can cover a plethora of use cases, ranging from the router in your house, a smart video camera surveying a parking lot, to a control... » read more

Dedicated Approximate Computing Framework To Efficiently Compute PCs On Hardware


A technical paper titled “On Hardware-efficient Inference in Probabilistic Circuits” was published by researchers at Aalto University and UCLouvain. Abstract: "Probabilistic circuits (PCs) offer a promising avenue to perform embedded reasoning under uncertainty. They support efficient and exact computation of various probabilistic inference tasks by design. Hence, hardware-efficient compu... » read more

MCU Changes At The Edge


Microcontrollers are becoming a key platform for processing machine learning at the edge due to two significant changes. First, they now can include multiple cores, including some for high performance and others for low power, as well as other specialized processing elements such as neural network accelerators. Second, machine learning algorithms have been pruned to the point where inferencing ... » read more

The Implications Of AI Everywhere: From Data Center To Edge


Generative AI has upped the ante on the transformative force of AI, driving profound implications across all aspects of our everyday lives. Over the past year, we have seen AI capabilities placed firmly in the hands of consumers. The recent news and product announcements emerging from MWC 2024 highlighted what we can expect to see from the next wave of generative AI applications. AI will be eve... » read more

RISC-V Ultra-Low-Power Edge Accelerators (EPFL)


A technical paper titled “X-HEEP: An Open-Source, Configurable and Extendible RISC-V Microcontroller for the Exploration of Ultra-Low-Power Edge Accelerators” was published by researchers at EPFL. Abstract: "The field of edge computing has witnessed remarkable growth owing to the increasing demand for real-time processing of data in applications. However, challenges persist due to limitat... » read more

Modeling Compute In Memory With Biological Efficiency


The growing popularity of generative AI, which uses natural language to help users make sense of unstructured data, is forcing sweeping changes in how compute resources are designed and deployed. In a panel discussion on artificial intelligence at last week’s IEEE Electron Device Meeting, IBM’s Nicole Saulnier described it as a major breakthrough that should allow AI tools to assist huma... » read more

Data Formats For Inference On The Edge


AI/ML training traditionally has been performed using floating point data formats, primarily because that is what was available. But this usually isn't a viable option for inference on the edge, where more compact data formats are needed to reduce area and power. Compact data formats use less space, which is important in edge devices, but the bigger concern is the power needed to move around... » read more

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