1
Triathlon of lightweight block ciphers for the Internet of things
2
Accelerated Visual Context Classification on a Low-Power Smartwatch
3
A high-efficiency runtime reconfigurable IP for CNN acceleration on a mid-range all-programmable SoC
4
Scalable EEG seizure detection on an ultra low power multi-core architecture
5
A Multi(bio)sensor Acquisition System With Integrated Processor, Power Management, $8 \times 8$ LED Drivers, and Simultaneously Synchronized ECG, BIO-Z, GSR, and Two PPG Readouts
6
Power, Area, and Performance Optimization of Standard Cell Memory Arrays Through Controlled Placement
7
A near-threshold RISC-V core with DSP extensions for scalable IoT Endpoint Devices
8
YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration
9
YodaNN: An Ultra-Low Power Convolutional Neural Network Accelerator Based on Binary Weights
10
A compact 446 Gbps/W AES accelerator for mobile SoC and IoT in 40nm
11
An energy harvesting wireless sensor node for IoT systems featuring a near-threshold voltage IA-32 microcontroller in 14nm tri-gate CMOS
12
Secure Data Analytics for Cloud-Integrated Internet of Things Applications
13
A heterogeneous multi-core system-on-chip for energy efficient brain inspired vision
14
A 20uA/MHz at 200MHz microcontroller with low power memory access scheme for small sensing nodes
15
193 MOPS/mW @ 162 MOPS, 0.32V to 1.15V voltage range multi-core accelerator for energy efficient parallel and sequential digital processing
16
A 1.3µW, 5pJ/cycle sub-threshold MSP430 processor in 90nm xLP FDSOI for energy-efficient IoT applications
17
14.6 A 1.42TOPS/W deep convolutional neural network recognition processor for intelligent IoE systems
18
14.5 Eyeriss: An energy-efficient reconfigurable accelerator for deep convolutional neural networks
19
Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks
20
Origami: A 803-GOp/s/W Convolutional Network Accelerator
21
Deep Residual Learning for Image Recognition
22
BinaryConnect: Training Deep Neural Networks with binary weights during propagations
23
A Versatile Embedded Platform for EMG Acquisition and Gesture Recognition
24
A 128-Channel Extreme Learning Machine-Based Neural Decoder for Brain Machine Interfaces
25
Novel Self-Body-Biasing and Statistical Design for Near-Threshold Circuits With Ultra Energy-Efficient AES as Case Study
26
PULP: A parallel ultra low power platform for next generation IoT applications
27
ShiDianNao: Shifting vision processing closer to the sensor
28
Accelerating real-time embedded scene labeling with convolutional networks
29
A convolutional neural network cascade for face detection
30
Robust Authenticated-Encryption AEZ and the Problem That It Solves
31
8.1 An 80nW retention 11.7pJ/cycle active subthreshold ARM Cortex-M0+ subsystem in 65nm CMOS for WSN applications
32
4.6 A1.93TOPS/W scalable deep learning/inference processor with tetra-parallel MIMD architecture for big-data applications
33
FaceNet: A unified embedding for face recognition and clustering
34
A ultra-low-energy convolution engine for fast brain-inspired vision in multicore clusters
35
Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks
36
340 mV–1.1 V, 289 Gbps/W, 2090-Gate NanoAES Hardware Accelerator With Area-Optimized Encrypt/Decrypt GF(2 4 ) 2 Polynomials in 22 nm Tri-Gate CMOS
37
Training deep neural networks with low precision multiplications
38
IoT Security: Ongoing Challenges and Research Opportunities
39
Brain-Inspired Classroom Occupancy Monitoring on a Low-Power Mobile Platform
40
Ultra-low-latency lightweight DMA for tightly coupled multi-core clusters
41
He-P2012: Architectural heterogeneity exploration on a scalable many-core platform
42
Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation
43
AEGIS: A Fast Authenticated Encryption Algorithm
44
A Low-Power Processor With Configurable Embedded Machine-Learning Accelerators for High-Order and Adaptive Analysis of Medical-Sensor Signals
45
Future Internet: The Internet of Things Architecture, Possible Applications and Key Challenges
46
ImageNet classification with deep convolutional neural networks
47
Context-Dependent Pre-Trained Deep Neural Networks for Large-Vocabulary Speech Recognition
48
A fully-synthesizable single-cycle interconnection network for Shared-L1 processor clusters
49
Harvesting the potential of nano-CMOS for lightweight cryptography: an ultra-low-voltage 65 nm AES coprocessor for passive RFID tags
50
The Security and Performance of the Galois/Counter Mode (GCM) of Operation
51
Efficient Instantiations of Tweakable Blockciphers and Refinements to Modes OCB and PMAC
52
Trends in Cardiac Pacemaker Batteries
53
Extreme Learning Machine : A Design Space Exploration
54
convnet-benchmarks Available: https: //github.com/soumith/convnet-benchmarks
55
Design of a low power SoC testchip for wearables and IoTs
56
INS-03510-C1 Datasheet, Datasheet of Myriad 2 Vision Processor
57
AES-GCM software performance on the current high end CPUs as a performance baseline for CAESAR competition
58
SleepWalker: A 25-MHz 0.4-V Sub- $\hbox{mm}^{2}$ 7- $\mu\hbox{W/MHz}$ Microcontroller in 65-nm LP/GP CMOS for Low-Carbon Wireless Sensor Nodes
59
Fog computing and its role in the Internet of Things
60
OpenRISC 1000 Architecture Manual
61
Advanced Encryption Standard (AES)
62
Intel Advanced Encryption Standard (AES) New Instructions Set . Santa Clara, CA, USA
63
Keccak sponge function family main document
64
National voluntary laboratory accreditation program: calibration laboratories; technical guide for mechanical measurements
65
Advanced Encryption Standard (AES)
66
NIST Special Publication 800-38E, accessed on
67
IoT ENDPOINT SYSTEM-ON-CHIP
68
Available: http://www.mobileye.com/ products/mobileye-5-series
69
“STMicroelectronics STM32L476xx Datasheet.”
70
Available: http: //ambiqmicro.com/apollo-ultra-low-power-mcu
71
www.st.com/resource/en/datasheet/stm32l476rg.pdf
72
on February 04,2023 at 18:47:11 UTC from IEEE Xplore. Restrictions apply
73
“Texas Instruments MSP430 Low-Power MCUs.”
74
“LPC54000 Series: Low Power Microcontollers (MCUs) based on ARM Cortex-M4 Cores with optional Cortex-M0+ co-processor.”
75
“Maxim Integrated MAXQ1061 DeepCover Cryptographic Controller for Embedded Devices.”
76
are with ETH Zürich, 8092 Zürich, Switzerland
77
Graz University of Technology, 8010 Graz, Austria, and also with the Know-Center, 8010 Graz, Austria
78
RealTimeLogic. SharkSSL/RayCrypto v2.4 crypto library benchmarks with ARM Cortex-M3 Available: https://realtimelogic.com/ products/sharkssl
79
“Apollo Ultra-Low-Power Microcontrollers.”
80
“CMSIS - Cortex Microcontroller Software Interface Standard.”
81
“Himax, emza and CEVA Partner to Create Ultra-Low Power, Always-On Vision Sensor for IoT.”
82
INS-03510-C1 Datasheet 2014, datasheet of Myriad 2 Vision Processor. [Online]. Available: http://uploads.movidius.com/ 1441734401-Myriad-2-product-brief
83
The Crazyflie Nano Quadcopter -Bitcraze
