tag: TinyML

Data Collection For Edge AI / Tiny ML With Sensors
By Renesas - 04 Oct, 2023 - Comments: 0

Reality AI software from Renesas provides solution suites and tools for R&D engineers who build products and internal solutions using sensors. Working with accelerometers, vibration, sound, electrical (current/voltage/ capacitance), radar, RF, proprietary sensors, and other types of sensor data, Reality AI software identifies signatures of events and conditions, correlates changes in signat... » read more

Patterns And Issues In AI Chip Design
By Ed Sperling - 14 Sep, 2023 - Comments: 2

AI is becoming more than a talking point for chip and system design, taking on increasingly complex tasks that are now competitive requirements in many markets. But the inclusion of AI, along with its machine learning and deep learning subcategories, also has injected widespread confusion and uncertainty into every aspect of electronics. This is partly due to the fact that it touches so many... » read more

Object Detection CNN Suitable For Edge Processors With Limited Memory
By Technical Paper Link - 14 Jun, 2023 - Comments: 0

A technical paper titled “TinyissimoYOLO: A Quantized, Low-Memory Footprint, TinyML Object Detection Network for Low Power Microcontrollers” was published by researchers at ETH Zurich. Abstract: "This paper introduces a highly flexible, quantized, memory-efficient, and ultra-lightweight object detection network, called TinyissimoYOLO. It aims to enable object detection on microcontrol... » read more

Week In Review: Auto, Security, Pervasive Computing
By Linda Christensen - 19 May, 2023 - Comments: 0

Former Apple engineer Weibao Wang was indicted for stealing Apple’s autonomous vehicle hardware and software IP and giving the information to Chinese competitors. Among other items, authorities said they found source code for the project on the engineer’s personal laptop, which was seized at his home. Wang fled to China the same day a search warrant was executed. This is one of five cases b... » read more

Low-Power Heterogeneous Compute Cluster For TinyML DNN Inference And On-Chip Training
By Technical Paper Link - 09 Apr, 2023 - Comments: 0

A new technical paper titled "DARKSIDE: A Heterogeneous RISC-V Compute Cluster for Extreme-Edge On-Chip DNN Inference and Training" was published by researchers at University of Bologna and ETH Zurich. Abstract "On-chip deep neural network (DNN) inference and training at the Extreme-Edge (TinyML) impose strict latency, throughput, accuracy, and flexibility requirements. Heterogeneous clus... » read more

Week In Review: Auto, Security, Pervasive Computing
By Linda Christensen - 10 Mar, 2023 - Comments: 0

Rambus will begin selling Arm's CryptoCell embedded security platform and CryptoIsland root-of-trust cores, setting the stage for a much broader push by Rambus into security for a wide range of connected devices, and ultimately into security as a service. Under the terms of the deal, Rambus' customers will be able to license Arm IP directly from Rambus. For Arm's existing customers, there will ... » read more

Complex Tradeoffs In Inferencing Chips
By John Koon - 17 Oct, 2022 - Comments: 0

Designing AI/ML inferencing chips is emerging as a huge challenge due to the variety of applications and the highly specific power and performance needs for each of them. Put simply, one size does not fit all, and not all applications can afford a custom design. For example, in retail store tracking, it's acceptable to have a 5% or 10% margin of error for customers passing by a certain aisle... » read more

Novel In-Pixel-in-Memory (P2M) Paradigm for Edge Intelligence (USC)
By Technical Paper Link - 24 Aug, 2022 - Comments: 0

A new technical paper titled "A processing-in-pixel-in-memory paradigm for resource-constrained TinyML applications" was published by researchers at University of Southern California (USC). According to the paper, "we propose a novel Processing-in-Pixel-in-memory (P2M) paradigm, that customizes the pixel array by adding support for analog multi-channel, multi-bit convolution, batch normaliza... » read more

CFU Playground: Significant Speedups & Design Space Exploration Between CPU & Accelerator
By Technical Paper Link - 30 May, 2022 - Comments: 0

Technical paper titled "CFU Playground: Full-Stack Open-Source Framework for Tiny Machine Learning (tinyML) Acceleration on FPGAs," from Google, Purdue University and Harvard University. Abstract "We present CFU Playground, a full-stack open-source framework that enables rapid and iterative design of machine learning (ML) accelerators for embedded ML systems. Our toolchain tightly integr... » read more

Deploying Artificial Intelligence At The Edge
By Pushkar Apte - 16 Sep, 2021 - Comments: 0

By Pushkar Apte and Tom Salmon Rapid advances in artificial intelligence (AI) have made this technology important for many industries, including finance, energy, healthcare, and microelectronics. AI is driving a multi-trillion-dollar global market while helping to solve some tough societal problems such as tracking the current pandemic and predicting the severity of climate-driven events lik... » read more

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