Dory: Automatic end-to-end deployment of real-world dnns on low-cost iot mcus
The deployment of Deep Neural Networks (DNNs) on end-nodes at the extreme edge of the
Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced …
Internet-of-Things is a critical enabler to support pervasive Deep Learning-enhanced …
Vega: A ten-core SoC for IoT endnodes with DNN acceleration and cognitive wake-up from MRAM-based state-retentive sleep mode
The Internet-of-Things (IoT) requires endnodes with ultra-low-power always-on capability for
a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility …
a long battery lifetime, as well as high performance, energy efficiency, and extreme flexibility …
A 64-mw dnn-based visual navigation engine for autonomous nano-drones
Fully miniaturized robots (eg, drones), with artificial intelligence (AI)-based visual navigation
capabilities, are extremely challenging drivers of Internet-of-Things edge intelligence …
capabilities, are extremely challenging drivers of Internet-of-Things edge intelligence …
Diana: An end-to-end hybrid digital and analog neural network soc for the edge
DIgital-ANAlog (DIANA), a heterogeneous multi-core accelerator, combines a reduced
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
instruction set computer-five (RISC-V) host processor with an analog in-memory computing …
T-CREST: Time-predictable multi-core architecture for embedded systems
Real-time systems need time-predictable platforms to allow static analysis of the worst-case
execution time (WCET). Standard multi-core processors are optimized for the average case …
execution time (WCET). Standard multi-core processors are optimized for the average case …
Variability mitigation in nanometer CMOS integrated systems: A survey of techniques from circuits to software
Variation in performance and power across manufactured parts and their operating
conditions is an accepted reality in modern microelectronic manufacturing processes with …
conditions is an accepted reality in modern microelectronic manufacturing processes with …
An IoT endpoint system-on-chip for secure and energy-efficient near-sensor analytics
Near-sensor data analytics is a promising direction for internet-of-things endpoints, as it
minimizes energy spent on communication and reduces network load-but it also poses …
minimizes energy spent on communication and reduces network load-but it also poses …
Arnold: An eFPGA-augmented RISC-V SoC for flexible and low-power IoT end nodes
A wide range of Internet of Things (IoT) applications require powerful, energy-efficient, and
flexible end nodes to acquire data from multiple sources, process and distill the sensed data …
flexible end nodes to acquire data from multiple sources, process and distill the sensed data …
A heterogeneous in-memory computing cluster for flexible end-to-end inference of real-world deep neural networks
Deployment of modern TinyML tasks on small battery-constrained IoT devices requires high
computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile …
computational energy efficiency. Analog In-Memory Computing (IMC) using non-volatile …
NEURAghe Exploiting CPU-FPGA Synergies for Efficient and Flexible CNN Inference Acceleration on Zynq SoCs
Deep convolutional neural networks (CNNs) obtain outstanding results in tasks that require
human-level understanding of data, like image or speech recognition. However, their …
human-level understanding of data, like image or speech recognition. However, their …