| Nome |
# |
|
Aging Effects of Leakage Optimizations for Caches, file e384c42e-0a64-d4b2-e053-9f05fe0a1d67
|
883
|
|
Modeling of thermally induced skew variations in clock distribution network, file e384c42e-0bf9-d4b2-e053-9f05fe0a1d67
|
614
|
|
Partitioned cache architectures for reduced NBTI-induced aging, file e384c42e-0bfb-d4b2-e053-9f05fe0a1d67
|
551
|
|
Power-Gating: More Than Leakage Savings, file e384c42e-0a65-d4b2-e053-9f05fe0a1d67
|
373
|
|
Approximate energy-efficient encoding for serial interfaces, file e384c42f-83fb-d4b2-e053-9f05fe0a1d67
|
339
|
|
Buffering of frequent accesses for reduced cache aging, file e384c42e-0bfa-d4b2-e053-9f05fe0a1d67
|
335
|
|
GIS-Based Optimal Photovoltaic Panel Floorplanning for Residential Installations, file e384c42f-dc80-d4b2-e053-9f05fe0a1d67
|
329
|
|
Ultra-low power circuits using graphene p-n junctions and adiabatic computing, file e384c42e-8353-d4b2-e053-9f05fe0a1d67
|
320
|
|
Zero-Transition Serial Encoding for Image Sensors, file e384c42f-83fd-d4b2-e053-9f05fe0a1d67
|
298
|
|
A methodology for the design of dynamic accuracy operators by runtime back bias, file e384c42f-8449-d4b2-e053-9f05fe0a1d67
|
247
|
|
IP-XACT for Smart Systems Design: Extensions for the Integration of Functional and Extra-Functional Models, file e384c431-c3f3-d4b2-e053-9f05fe0a1d67
|
222
|
|
A Compact PV Panel Model for Cyber-Physical Systems in Smart Cities, file e384c430-34b4-d4b2-e053-9f05fe0a1d67
|
185
|
|
Empirical derivation of upper and lower bounds of NBTI aging for embedded cores, file e384c42f-88d0-d4b2-e053-9f05fe0a1d67
|
180
|
|
An aging-aware battery charge scheme for mobile devices exploiting plug-in time patterns, file e384c431-96dd-d4b2-e053-9f05fe0a1d67
|
153
|
|
Composable battery model templates based on manufacturers’ data, file e384c431-c9d9-d4b2-e053-9f05fe0a1d67
|
133
|
|
Quasi-Adiabatic Logic Arrays for Silicon and Beyond-Silicon Energy-Efficient ICs, file e384c431-a2ff-d4b2-e053-9f05fe0a1d67
|
128
|
|
Aging and cost optimal residential charging for plug-in EVs, file e384c431-9a31-d4b2-e053-9f05fe0a1d67
|
127
|
|
Optimal Content-Dependent Dynamic Brightness Scaling for OLED Displays, file e384c431-934b-d4b2-e053-9f05fe0a1d67
|
109
|
|
Fast thermal simulation using SystemC-AMS, file e384c431-b2f3-d4b2-e053-9f05fe0a1d67
|
108
|
|
An Automated Design Flow for Approximate Circuits based on Reduced Precision Redundancy, file e384c431-934f-d4b2-e053-9f05fe0a1d67
|
106
|
|
A unified model of power sources for the simulation of electrical energy systems, file e384c431-c3ee-d4b2-e053-9f05fe0a1d67
|
100
|
|
Multi-function logic synthesis of silicon and beyond-silicon ultra-low power pass-gates circuits, file e384c431-a300-d4b2-e053-9f05fe0a1d67
|
96
|
|
Optimal Configuration and Placement of PV Systems in Building Roofs with Cost Analysis, file e384c432-2d38-d4b2-e053-9f05fe0a1d67
|
89
|
|
A temperature-aware battery cycle life model for different battery chemistries, file e384c431-b0dc-d4b2-e053-9f05fe0a1d67
|
87
|
|
CONTREX: Design of embedded mixed-criticality CONTRol systems under consideration of EXtra-functional properties, file e384c431-b18f-d4b2-e053-9f05fe0a1d67
|
82
|
|
A Comparison Analysis of BLE-Based Algorithms for Localization in Industrial Environments, file e384c431-3b09-d4b2-e053-9f05fe0a1d67
|
79
|
|
A Smart Meter Infrastructure for Smart Grid IoT Applications, file e384c434-426e-d4b2-e053-9f05fe0a1d67
|
74
|
|
Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles, file e384c431-7971-d4b2-e053-9f05fe0a1d67
|
73
|
|
CRIME: Input-Dependent Collaborative Inference for Recurrent Neural Networks, file e384c432-3e29-d4b2-e053-9f05fe0a1d67
|
67
|
|
Fine-grain Back Biasing for the Design of Energy-Quality Scalable Operators, file e384c430-293e-d4b2-e053-9f05fe0a1d67
|
65
|
|
Logic Synthesis for Silicon and Beyond-Silicon Multi-gate Pass-Logic Circuits, file e384c431-7266-d4b2-e053-9f05fe0a1d67
|
65
|
|
Modeling and Simulation of Cyber-Physical Electrical Energy Systems with SystemC-AMS, file e384c431-d2c1-d4b2-e053-9f05fe0a1d67
|
65
|
|
A case for a battery-aware model of drone energy consumption, file e384c431-9a3b-d4b2-e053-9f05fe0a1d67
|
63
|
|
Manufacturing as a Data-Driven Practice: Methodologies, Technologies, and Tools, file e384c432-a64a-d4b2-e053-9f05fe0a1d67
|
61
|
|
Battery-aware energy model of drone delivery tasks, file e384c431-9622-d4b2-e053-9f05fe0a1d67
|
59
|
|
An equation-based battery cycle life model for various battery chemistries, file e384c431-b0e4-d4b2-e053-9f05fe0a1d67
|
53
|
|
One-pass logic synthesis for graphene-based Pass-XNOR logic circuits, file e384c42e-827f-d4b2-e053-9f05fe0a1d67
|
52
|
|
Reducing the Energy Consumption of sEMG-Based Gesture Recognition at the Edge Using Transformers and Dynamic Inference, file 8b6be7f7-5ca5-4068-b30b-46ccf4e07e10
|
48
|
|
Optimal Input-Dependent Edge-Cloud Partitioning for RNN Inference, file e384c431-b3a2-d4b2-e053-9f05fe0a1d67
|
48
|
|
Low-Overhead Adaptive Brightness Scaling for Energy Reduction in OLED Displays, file e384c430-a5d7-d4b2-e053-9f05fe0a1d67
|
47
|
|
Design of District-level Photovoltaic Installations for Optimal Power Production and Economic Benefit, file e384c434-13f0-d4b2-e053-9f05fe0a1d67
|
47
|
|
Input-dependent edge-cloud mapping of recurrent neural networks inference, file e384c432-c6fa-d4b2-e053-9f05fe0a1d67
|
46
|
|
ACME: An energy-efficient approximate bus encoding for I2C, file e384c433-ed50-d4b2-e053-9f05fe0a1d67
|
46
|
|
Sequence-To-Sequence Neural Networks Inference on Embedded Processors Using Dynamic Beam Search, file e384c431-c9d8-d4b2-e053-9f05fe0a1d67
|
45
|
|
A Microservices-based Framework for Smart Design and Optimization of PV Installations, file e384c432-3d5d-d4b2-e053-9f05fe0a1d67
|
44
|
|
Ultra-compact binary neural networks for human activity recognition on RISC-V processors, file e384c433-e13e-d4b2-e053-9f05fe0a1d67
|
44
|
|
A Layered Methodology for the Simulation of Extra-Functional Properties in Smart Systems, file e384c431-a012-d4b2-e053-9f05fe0a1d67
|
42
|
|
Automated Synthesis of Energy-Efficient Reconfigurable-Precision Circuits, file e384c431-5a1a-d4b2-e053-9f05fe0a1d67
|
39
|
|
Application-Driven Synthesis of Energy-Efficient Reconfigurable-Precision Operators, file e384c430-2940-d4b2-e053-9f05fe0a1d67
|
38
|
|
SystemC-AMS Simulation of Energy Management of Electric Vehicles, file e384c431-b185-d4b2-e053-9f05fe0a1d67
|
37
|
|
SystemC-AMS thermal modeling for the co-simulation of functional and extra-functional properties, file e384c431-9dba-d4b2-e053-9f05fe0a1d67
|
35
|
|
Electric Vehicles Plug-In Duration Forecasting Using Machine Learning for Battery Optimization, file e384c432-3bf9-d4b2-e053-9f05fe0a1d67
|
33
|
|
Optimal battery sizing for electric truck delivery, file e384c431-a509-d4b2-e053-9f05fe0a1d67
|
31
|
|
Energy-efficient digital processing via Approximate Computing, file e384c431-934c-d4b2-e053-9f05fe0a1d67
|
28
|
|
A Cross-Level Verification Methodology for Digital IPs Augmented with Embedded Timing Monitors, file e384c431-a186-d4b2-e053-9f05fe0a1d67
|
28
|
|
Energy-efficient adaptive machine learning on IoT end-nodes with class-dependent confidence, file e384c433-acbe-d4b2-e053-9f05fe0a1d67
|
28
|
|
Assessing the impact of sensor-based task scheduling on battery lifetime in IoT devices, file e384c433-cb1e-d4b2-e053-9f05fe0a1d67
|
28
|
|
Cost-aware design and simulation of electrical energy systems, file e384c432-806b-d4b2-e053-9f05fe0a1d67
|
26
|
|
Logic Synthesis of Pass-Gate Logic Circuits with Emerging Ambipolar Technologies, file e384c431-7264-d4b2-e053-9f05fe0a1d67
|
25
|
|
A SystemC-AMS Framework for the Design and Simulation of Energy Management in Electric Vehicles, file e384c431-c2b8-d4b2-e053-9f05fe0a1d67
|
25
|
|
CNN-Based Camera-less User Attention Detection for Smartphone Power Management, file e384c431-b0c6-d4b2-e053-9f05fe0a1d67
|
24
|
|
TCN Mapping Optimization for Ultra-Low Power Time-Series Edge Inference, file e384c433-e665-d4b2-e053-9f05fe0a1d67
|
24
|
|
Power-gating for leakage control and beyond, file e384c431-9c14-d4b2-e053-9f05fe0a1d67
|
21
|
|
Battery-aware electric truck delivery route planner, file e384c431-8e1d-d4b2-e053-9f05fe0a1d67
|
19
|
|
Characterizing the activity factor in NBTI aging models for embedded cores, file e384c431-c704-d4b2-e053-9f05fe0a1d67
|
19
|
|
Dynamic Beam Width Tuning for Energy-Efficient Recurrent Neural Networks, file e384c431-9cf9-d4b2-e053-9f05fe0a1d67
|
18
|
|
Dynamic bit-width reconfiguration for energy-efficient deep learning hardware, file e384c431-b3a6-d4b2-e053-9f05fe0a1d67
|
18
|
|
Battery-aware design exploration of scheduling policies for multi-sensor devices, file e384c431-7417-d4b2-e053-9f05fe0a1d67
|
17
|
|
Irradiance-Driven Partial Reconfiguration of PV Panels, file e384c431-c1ec-d4b2-e053-9f05fe0a1d67
|
17
|
|
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling, file e384c434-d214-d4b2-e053-9f05fe0a1d67
|
17
|
|
A Li-ion battery charge protocol with optimal aging-quality of service trade-off, file e384c431-7ae6-d4b2-e053-9f05fe0a1d67
|
16
|
|
Graphene-PLA (GPLA): A compact and ultra-low power logic array architecture, file e384c431-b249-d4b2-e053-9f05fe0a1d67
|
16
|
|
A Semi-Empirical Model of PV Modules Including Manufacturing I-V Mismatch, file e384c431-b184-d4b2-e053-9f05fe0a1d67
|
15
|
|
Battery-aware electric truck delivery route exploration, file e384c432-6a26-d4b2-e053-9f05fe0a1d67
|
15
|
|
A Top-down Constraint-driven Methodology for Smart System Design, file e384c42e-2873-d4b2-e053-9f05fe0a1d67
|
14
|
|
Exploiting the Expressive Power of Graphene Reconfigurable Gates via Post-Synthesis Optimization, file e384c42e-8354-d4b2-e053-9f05fe0a1d67
|
13
|
|
Privacy-preserving Social Distance Monitoring on Microcontrollers with Low-Resolution Infrared Sensors and CNNs, file bd4d2aeb-971c-4067-9ba8-92f63e0159c1
|
12
|
|
On the impact of smart sensor approximations on the accuracy of machine learning tasks, file e384c432-e35a-d4b2-e053-9f05fe0a1d67
|
12
|
|
Digital Transformation of a Production Line: Network Design, Online Data Collection and Energy Monitoring, file e384c434-248c-d4b2-e053-9f05fe0a1d67
|
12
|
|
Predicting Hard Disk Failures in Data Centers Using Temporal Convolutional Neural Networks, file e384c433-fc00-d4b2-e053-9f05fe0a1d67
|
11
|
|
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks, file fdcf5792-f5ac-46cb-92e6-85374bc66224
|
11
|
|
Optimal Topology-Aware PV Panel Floorplanning with Hybrid Orientation, file e384c431-7739-d4b2-e053-9f05fe0a1d67
|
10
|
|
Battery-Aware Operation Range Estimation for Terrestrial and Aerial Electric Vehicles, file e384c431-8b65-d4b2-e053-9f05fe0a1d67
|
10
|
|
Low-overhead power trace obfuscation for smart meter privacy, file e384c431-a71e-d4b2-e053-9f05fe0a1d67
|
10
|
|
Energy-efficient coordinated electric truck-drone hybrid delivery service planning, file e384c432-8d22-d4b2-e053-9f05fe0a1d67
|
10
|
|
Adaptive Random Forests for Energy-Efficient Inference on Microcontrollers, file 4c78e1a8-1a50-49f5-9a9d-7d37f46310db
|
8
|
|
Human Activity Recognition on Microcontrollers with Quantized and Adaptive Deep Neural Networks, file d879d52b-a9f4-4594-894b-67676cb37005
|
8
|
|
Efficient Deep Learning Models for Privacy-preserving People Counting on Low-resolution Infrared Arrays, file e132c370-f0e6-48ff-bfac-677176a778a1
|
8
|
|
Assessing the dependence of li-Ion batteries capacity on load current frequency, file 8171ded1-2653-4908-a65e-3c2d13a82b42
|
7
|
|
Low-Overhead Early-Stopping Policies for Efficient Random Forests Inference on Microcontrollers, file a7ea62fd-76c5-4ac1-a8e3-61e07a29511e
|
7
|
|
Quality inspection of critical aircraft engine components: towards full automation, file b19a9e2d-428e-4b0f-8692-bdb618eca430
|
7
|
|
Lightweight Neural Architecture Search for Temporal Convolutional Networks at the Edge, file c6857794-1497-42c2-8254-52436674840b
|
7
|
|
Embedding Temporal Convolutional Networks for Energy-efficient PPG-based Heart Rate Monitoring, file a62acdc8-0cfa-441a-b651-b7ad7b0dde88
|
6
|
|
All-digital embedded meters for on-line power estimation, file e384c430-293f-d4b2-e053-9f05fe0a1d67
|
6
|
|
Dynamic bit-width reconfiguration for energy-efficient deep learning hardware, file e384c431-bc5a-d4b2-e053-9f05fe0a1d67
|
6
|
|
LAPSE: Low-Overhead Adaptive Power Saving and Contrast Enhancement for OLEDs, file e384c433-12a8-d4b2-e053-9f05fe0a1d67
|
6
|
|
Multi-Complexity-Loss DNAS for Energy-Efficient and Memory-Constrained Deep Neural Networks, file a4a70868-43b4-4545-9943-296876c7c65c
|
5
|
|
In-Situ Monitoring of Additive Manufacturing, file e384c433-b8d1-d4b2-e053-9f05fe0a1d67
|
5
|
|
In-Situ Monitoring of Additive Manufacturing, file e384c433-cad2-d4b2-e053-9f05fe0a1d67
|
5
|
|
Ultra-compact binary neural networks for human activity recognition on RISC-V processors, file e384c433-e13f-d4b2-e053-9f05fe0a1d67
|
5
|
| Totale |
8.305 |
Archivio Istituzionale della Ricerca