TAGLIAVINI, GIUSEPPE
 Distribuzione geografica
Continente #
NA - Nord America 2.079
EU - Europa 1.973
AS - Asia 1.124
AF - Africa 48
SA - Sud America 45
OC - Oceania 15
Continente sconosciuto - Info sul continente non disponibili 6
Totale 5.290
Nazione #
US - Stati Uniti d'America 2.018
IT - Italia 486
CN - Cina 340
FR - Francia 313
DE - Germania 227
NL - Olanda 162
IN - India 138
GB - Regno Unito 132
JP - Giappone 112
IE - Irlanda 94
RU - Federazione Russa 91
HK - Hong Kong 89
SG - Singapore 89
TW - Taiwan 73
CZ - Repubblica Ceca 66
KR - Corea 64
IR - Iran 59
CA - Canada 57
UA - Ucraina 55
CH - Svizzera 52
ES - Italia 42
GR - Grecia 36
FI - Finlandia 31
BE - Belgio 28
PL - Polonia 27
BG - Bulgaria 24
TR - Turchia 24
MY - Malesia 22
SE - Svezia 22
EG - Egitto 21
VN - Vietnam 20
RO - Romania 19
BR - Brasile 18
PT - Portogallo 17
ID - Indonesia 16
CO - Colombia 12
PK - Pakistan 12
IL - Israele 11
LU - Lussemburgo 11
AT - Austria 10
AU - Australia 10
PH - Filippine 10
ZA - Sudafrica 10
SA - Arabia Saudita 8
DK - Danimarca 7
JO - Giordania 7
MA - Marocco 7
MO - Macao, regione amministrativa speciale della Cina 7
AR - Argentina 6
RS - Serbia 6
AE - Emirati Arabi Uniti 5
NZ - Nuova Zelanda 5
BD - Bangladesh 4
CL - Cile 4
EU - Europa 4
IQ - Iraq 3
LB - Libano 3
MU - Mauritius 3
MX - Messico 3
NG - Nigeria 3
NO - Norvegia 3
TH - Thailandia 3
AL - Albania 2
LK - Sri Lanka 2
LT - Lituania 2
MG - Madagascar 2
PE - Perù 2
SI - Slovenia 2
SK - Slovacchia (Repubblica Slovacca) 2
A1 - Anonimo 1
AM - Armenia 1
BA - Bosnia-Erzegovina 1
BH - Bahrain 1
BO - Bolivia 1
CI - Costa d'Avorio 1
CU - Cuba 1
EC - Ecuador 1
HU - Ungheria 1
IS - Islanda 1
LV - Lettonia 1
NP - Nepal 1
PY - Paraguay 1
TN - Tunisia 1
XK - ???statistics.table.value.countryCode.XK??? 1
Totale 5.290
Città #
Ashburn 288
Houston 217
Bologna 188
Fairfield 117
Ann Arbor 109
Dublin 89
Paris 86
Santa Cruz 74
Seattle 67
Buffalo 66
Beijing 54
Singapore 54
Wilmington 54
Boardman 45
Tokyo 44
Woodbridge 44
Cambridge 43
Bengaluru 42
Shanghai 42
Milan 41
Eindhoven 37
Taipei 35
Los Angeles 33
Zurich 33
Chicago 28
San Diego 25
Central 24
Norwalk 24
New York 23
Sofia 22
Guangzhou 21
Wuhan 21
Council Bluffs 19
Munich 19
Southend 18
Amsterdam 17
Hangzhou 17
Redmond 17
Toronto 17
Grenoble 16
Enschede 15
Moscow 15
Ottawa 15
San Jose 15
Athens 14
London 14
Turin 14
Frankfurt am Main 13
Berlin 12
Dresden 12
Istanbul 12
Las Vegas 12
Sunnyvale 12
Barcelona 11
Central District 11
Jinan 11
Milpitas 11
New Taipei 11
Tehran 11
Zhengzhou 11
Cairo 10
Helsinki 10
Lake Forest 10
Madrid 10
Polska 10
Shenzhen 10
Atlanta 9
Brussels 9
Falls Church 9
Lormont 9
Mountain View 9
University Park 9
Cartagena 8
Cedar Knolls 8
Changsha 8
Dong Ket 8
Esch-sur-Alzette 8
Kolkata 8
Muizenberg 8
Nanjing 8
Palermo 8
Rochester 8
San Francisco 8
Austin 7
Henderson 7
Ho Chi Minh City 7
Kochi 7
Kollam 7
Petaling Jaya 7
Phoenix 7
Antioch 6
Chengdu 6
Coimbatore 6
Dorsten 6
Durham 6
Ferrara 6
Forlì 6
Germantown 6
Higashimurayama 6
Jakarta 6
Totale 2.793
Nome #
Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing, file e1dcb333-42fc-7715-e053-1705fe0a6cc9 786
Design and Evaluation of SmallFloat SIMD extensions to the RISC-V ISA, file e1dcb332-f35e-7715-e053-1705fe0a6cc9 500
FlexFloat: A Software Library for Transprecision Computing, file e1dcb332-7ac8-7715-e053-1705fe0a6cc9 475
The transprecision computing paradigm: Concept, design, and applications, file e1dcb331-2a95-7715-e053-1705fe0a6cc9 470
A Transprecision Floating-Point Architecture for Energy-Efficient Embedded Computing, file e1dcb331-e797-7715-e053-1705fe0a6cc9 340
A Transprecision Floating-Point Platform for Ultra-Low Power Computing, file e1dcb331-4859-7715-e053-1705fe0a6cc9 276
Unleashing Fine-Grained Parallelism on Embedded Many-Core Accelerators with Lightweight OpenMP Tasking, file e1dcb332-f841-7715-e053-1705fe0a6cc9 259
ADRENALINE: An OpenVX Environment to Optimize Embedded Vision Applications on Many-core Accelerators, file e1dcb335-2ef7-7715-e053-1705fe0a6cc9 187
Synergistic HW/SW Approximation Techniques for Ultralow-Power Parallel Computing, file e1dcb332-eb49-7715-e053-1705fe0a6cc9 185
Streamlining the OpenMP Programming Model on Ultra-Low-Power Multi-core MCUs, file e1dcb338-34b7-7715-e053-1705fe0a6cc9 180
4.4 A 1.3TOPS/W @ 32GOPS Fully Integrated 10-Core SoC for IoT End-Nodes with 1.7μW Cognitive Wake-Up from MRAM-Based State-Retentive Sleep Mode, file e1dcb337-bec6-7715-e053-1705fe0a6cc9 176
A Low-Power Transprecision Floating-Point Cluster for Efficient Near-Sensor Data Analytics, file e1dcb33a-0e0f-7715-e053-1705fe0a6cc9 167
A mixed-precision RISC-V processor for extreme-edge DNN inference, file e1dcb336-ab13-7715-e053-1705fe0a6cc9 131
XpulpNN: Accelerating Quantized Neural Networks on RISC-V Processors Through ISA Extensions, file 07e66c35-3fe2-4526-9870-de331f8b5284 125
HULK-V: a Heterogeneous Ultra-low-power Linux capable RISC-V SoC, file 443324f8-2dda-4e5d-b2ce-1e8f317a984e 117
Combining learning and optimization for transprecision computing, file e1dcb335-b9e8-7715-e053-1705fe0a6cc9 107
DORY: Automatic End-to-End Deployment of Real-World DNNs on Low-Cost IoT MCUs, file e1dcb33a-1eef-7715-e053-1705fe0a6cc9 105
Optimizing Random Forest Based Inference on RISC-V MCUs at the Extreme Edge, file 9173cef3-0f7f-42af-ba39-8a7ca9207d0b 96
XpulpNN: Enabling Energy Efficient and Flexible Inference of Quantized Neural Networks on RISC-V Based IoT End Nodes, file e1dcb338-f289-7715-e053-1705fe0a6cc9 92
Enabling mixed-precision quantized neural networks in extreme-edge devices, file e1dcb335-b944-7715-e053-1705fe0a6cc9 88
Energy-Efficient Hardware-Accelerated Synchronization for Shared-L1-Memory Multiprocessor Clusters, file e1dcb336-3cd9-7715-e053-1705fe0a6cc9 84
PULP-TrainLib: Enabling On-Device Training for RISC-V Multi-core MCUs Through Performance-Driven Autotuning, file a0e65690-4a99-494f-896e-e6fc8b9f6efc 81
A 1.15 TOPS/W, 16-Cores Parallel Ultra-Low Power Cluster with 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode, file e1dcb338-e6bc-7715-e053-1705fe0a6cc9 78
GVSoC: A Highly Configurable, Fast and Accurate Full-Platform Simulator for RISC-V based IoT Processors, file e1dcb338-a1d9-7715-e053-1705fe0a6cc9 42
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices, file 155fc9c4-6728-4a00-bd5e-986f6497f5c5 37
Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes, file b2290547-0b4e-41b3-ba51-6ebaa2a80fea 35
TRANSPIRE: An energy-efficient TRANSprecision floating-point Programmable archItectuRE, file ccaff54d-79b3-41b5-815f-90b60ebe6e66 32
Vega: A Ten-Core SoC for IoT Endnodes with DNN Acceleration and Cognitive Wake-Up from MRAM-Based State-Retentive Sleep Mode, file e1dcb338-9c48-7715-e053-1705fe0a6cc9 28
Optimizing memory bandwidth in OpenVX graph execution on embedded many-core accelerators, file e1dcb32d-0c23-7715-e053-1705fe0a6cc9 27
Scale up your In-Memory Accelerator: Leveraging Wireless-on-Chip Communication for AIMC-based CNN Inference, file b4c4f96b-4b18-463d-b94d-ab774f4dceec 12
Source Code Classification for Energy Efficiency in Parallel Ultra Low-Power Microcontrollers, file 78a3022c-e107-4453-bfc0-c781eafd74ee 11
DNN Is Not All You Need: Parallelizing Non-neural ML Algorithms on Ultra-low-power IoT Processors, file 8bb2c1fa-ee8d-4b34-811e-2d3f655e78d1 11
Tightly-coupled hardware support to dynamic parallelism acceleration in embedded shared memory clusters, file e1dcb32d-06c2-7715-e053-1705fe0a6cc9 11
ADRENALINE: An OpenVX Environment to Optimize Embedded Vision Applications on Many-core Accelerators, file e1dcb32d-e02c-7715-e053-1705fe0a6cc9 9
An Optimized Heart Rate Detection System Based on Low-Power Microcontroller Platforms for Biosignal Processing, file 1ddc3b73-e8dd-4bc9-9e5d-022a612fa1fd 8
Always-on motion detection with application-level error control on a near-threshold approximate computing platform, file e1dcb32f-60a0-7715-e053-1705fe0a6cc9 7
A Transprecision Floating-Point Architecture for Energy-Efficient Embedded Computing, file e1dcb331-a6fe-7715-e053-1705fe0a6cc9 6
Synergistic architecture and programming model support for approximate micropower computing, file e1dcb32d-e02e-7715-e053-1705fe0a6cc9 5
Enabling mixed-precision quantized neural networks in extreme-edge devices, file e1dcb335-a256-7715-e053-1705fe0a6cc9 5
PULP-TrainLib: Enabling On-Device Training for RISC-V Multi-core MCUs Through Performance-Driven Autotuning, file 42c9e8f9-229f-47e3-b52e-6a4a9b7a217d 4
Supporting localized openvx kernel execution for efficient computer vision application development on sthorm many-core platform, file e1dcb32d-0743-7715-e053-1705fe0a6cc9 4
Optimizing Self-Organizing Maps for Bacterial Genome Identification on Parallel Ultra-Low-Power Platforms, file 6d8a8726-147b-49f8-b866-4ea1cc93c492 3
Scalable Hierarchical Instruction Cache for Ultralow-Power Processors Clusters, file c1a1bc45-dcbb-4b31-aa10-6fb0442c8d84 3
The transprecision computing paradigm: Concept, design, and applications, file e1dcb331-2a96-7715-e053-1705fe0a6cc9 3
BioWolf: A Sub-10-mW 8-Channel Advanced Brain-Computer Interface Platform with a Nine-Core Processor and BLE Connectivity, file e1dcb333-7d0e-7715-e053-1705fe0a6cc9 3
ANGELS - Smart Steering Wheel for Driver Safety, file 3a8270d1-67b6-4bb0-9495-3cbdf4cafd4f 2
Optimizing Self-Organizing Maps for Bacterial Genome Identification on Parallel Ultra-Low-Power Platforms, file b2b76dd3-a7c3-4f0a-8250-7b33ff6b2ee9 2
Enabling Fine-Grained OpenMP Tasking on Tightly-Coupled Shared Memory Clusters, file e1dcb32c-5329-7715-e053-1705fe0a6cc9 2
PULP: A parallel ultra low power platform for next generation IoT applications, file e1dcb32e-e619-7715-e053-1705fe0a6cc9 2
A Transprecision Floating-Point Platform for Ultra-Low Power Computing, file e1dcb331-4858-7715-e053-1705fe0a6cc9 2
A Low-Power Transprecision Floating-Point Cluster for Efficient Near-Sensor Data Analytics, file e1dcb339-c252-7715-e053-1705fe0a6cc9 2
Towards Long-term Non-invasive Monitoring for Epilepsy via Wearable EEG Devices, file e1dcb339-c63e-7715-e053-1705fe0a6cc9 2
End-to-End DNN Inference on a Massively Parallel Analog In Memory Computing Architecture, file 0cf2866b-983e-45f0-bcb8-29cb9f8cfff8 1
Dustin: A 16-Cores Parallel Ultra-Low-Power Cluster With 2b-to-32b Fully Flexible Bit-Precision and Vector Lockstep Execution Mode, file 991991f1-1545-4d39-8ca8-ea03d72e80c6 1
Efficient Transform Algorithms for Parallel Ultra-Low-Power IoT End Nodes, file a9955cce-b4b7-4e1e-9e08-8e167202f860 1
MPOpt-Cell: a high-performance data-flow programming environment for the CELL BE processor, file e1dcb32c-3e7a-7715-e053-1705fe0a6cc9 1
Energy efficient parallel computing on the PULP platform with support for OpenMP, file e1dcb32c-b95a-7715-e053-1705fe0a6cc9 1
Simplifying Many-Core-Based Heterogeneous SoC Programming with Offload Directives, file e1dcb32e-f36e-7715-e053-1705fe0a6cc9 1
Design and Evaluation of SmallFloat SIMD extensions to the RISC-V ISA, file e1dcb332-f35f-7715-e053-1705fe0a6cc9 1
Mr.Wolf: An Energy-Precision Scalable Parallel Ultra Low Power SoC for IoT Edge Processing, file e1dcb333-3d4d-7715-e053-1705fe0a6cc9 1
Combining learning and optimization for transprecision computing, file e1dcb335-ecf0-7715-e053-1705fe0a6cc9 1
DNN Is Not All You Need: Parallelizing Non-neural ML Algorithms on Ultra-low-power IoT Processors, file f759ae00-094c-4009-be79-3d593798e645 1
Totale 5.435
Categoria #
all - tutte 11.250
article - articoli 0
book - libri 0
conference - conferenze 0
curatela - curatele 0
other - altro 0
patent - brevetti 0
selected - selezionate 0
volume - volumi 0
Totale 11.250


Totale Lug Ago Sett Ott Nov Dic Gen Feb Mar Apr Mag Giu
2018/20193 0 0 0 0 0 0 0 0 0 0 1 2
2019/2020549 1 4 2 34 43 53 67 64 86 63 87 45
2020/2021958 71 79 48 80 64 84 85 77 53 125 97 95
2021/20221.221 87 40 61 229 169 58 76 95 49 85 168 104
2022/20231.211 66 111 139 112 75 70 90 84 135 83 145 101
2023/20241.457 130 140 102 163 153 181 210 278 96 0 4 0
Totale 5.435