Efficient TNN Inference on RISC-V Processing Cores With Minimal HW Overhead

A new technical paper titled "xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems" was published by researchers at ETH Zurich and Universita di Bologna. Abstract "Ternary neural networks (TNNs) offer a superior accuracy-energy trade-off compared to binary neural networks. However, until now, they have required specialized accelerators to realize their effic... » read more

MIPI Deployment In Ultra-Low-Power Streaming Sensors

Streams of data from higher-speed sensors pose throughput and latency challenges for designers. However, optimizing a design for those criteria can come at the expense of increased power consumption if not conceived and executed carefully. A device like a high-resolution, high-frame-rate home security camera in a non-wired application requiring frequent battery changes or recharging will likely... » 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

A Hierarchical Instruction Cache Tailored To Ultra-Low-Power Tightly-Coupled Processor Clusters

A technical paper titled “Scalable Hierarchical Instruction Cache for Ultra-Low-Power Processors Clusters” was published by researchers at University of Bologna, ETH Zurich, and GreenWaves Technologies. Abstract: "High Performance and Energy Efficiency are critical requirements for Internet of Things (IoT) end-nodes. Exploiting tightly-coupled clusters of programmable processors (CMPs) ha... » read more

SB MOSFET-Based Ultra-Low Power Real-Time Neurons for Neuromorphic Computing (Indian Institute of Technology)

A technical paper titled “Schottky Barrier MOSFET Enabled Ultra-Low Power Real-Time Neuron for Neuromorphic Computing” was published by researchers at the Indian Institute of Technology (IIT) Bombay. Abstract: "Energy-efficient real-time synapses and neurons are essential to enable large-scale neuromorphic computing. In this paper, we propose and demonstrate the Schottky-Barrier MOSFE... » read more

Low-Power Heterogeneous Compute Cluster For TinyML DNN Inference And On-Chip Training

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

Heterogeneous Ultra-Low-Power RISC-V SoC Running Linux

A technical paper titled "HULK-V: a Heterogeneous Ultra-low-power Linux capable RISC-V SoC" was published by researchers at University of Bologna, University of Modena and Reggio Emilia, and ETH Zurich. "We present HULK-V: an open-source Heterogeneous Linux-capable RISC-V-based SoC coupling a 64-bit RISC-V processor with an 8-core Programmable Multi-Core Accelerator (PMCA), delivering up to... » read more

New Way To Control Spin Currents At Room Temperature

New technical paper titled "Spin manipulation by giant valley-Zeeman spin-orbit field in atom-thick WSe2." from researchers at Beihang University (China) and University of British Columbia. Abstract: "The phenomenon originating from spin–orbit coupling provides energy-efficient strategies for spin manipulation and device applications. The broken inversion symmetry interface and the result... » read more

Always-On, Ultra-Low-Power Design Gains Traction

A surge of electronic devices powered by batteries, combined with ever-increasing demand for more features, intelligence, and performance, is putting a premium on chip designs that require much lower power. This is especially true for always-on circuits, which are being added into AR/VR, automotive applications with over-the-air updates, security cameras, drones, and robotics. Also known as ... » read more

Wavelength Multiplexed Ultralow-Power Photonic Edge Computing

Abstract "Advances in deep neural networks (DNNs) are transforming science and technology. However, the increasing computational demands of the most powerful DNNs limit deployment on low-power devices, such as smartphones and sensors -- and this trend is accelerated by the simultaneous move towards Internet-of-Things (IoT) devices. Numerous efforts are underway to lower power consumption, but ... » read more

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