4,400 research outputs found

    An IoT Endpoint System-on-Chip for Secure and Energy-Efficient Near-Sensor Analytics

    Full text link
    Near-sensor data analytics is a promising direction for IoT endpoints, as it minimizes energy spent on communication and reduces network load - but it also poses security concerns, as valuable data is stored or sent over the network at various stages of the analytics pipeline. Using encryption to protect sensitive data at the boundary of the on-chip analytics engine is a way to address data security issues. To cope with the combined workload of analytics and encryption in a tight power envelope, we propose Fulmine, a System-on-Chip based on a tightly-coupled multi-core cluster augmented with specialized blocks for compute-intensive data processing and encryption functions, supporting software programmability for regular computing tasks. The Fulmine SoC, fabricated in 65nm technology, consumes less than 20mW on average at 0.8V achieving an efficiency of up to 70pJ/B in encryption, 50pJ/px in convolution, or up to 25MIPS/mW in software. As a strong argument for real-life flexible application of our platform, we show experimental results for three secure analytics use cases: secure autonomous aerial surveillance with a state-of-the-art deep CNN consuming 3.16pJ per equivalent RISC op; local CNN-based face detection with secured remote recognition in 5.74pJ/op; and seizure detection with encrypted data collection from EEG within 12.7pJ/op.Comment: 15 pages, 12 figures, accepted for publication to the IEEE Transactions on Circuits and Systems - I: Regular Paper

    A 64mW DNN-based Visual Navigation Engine for Autonomous Nano-Drones

    Full text link
    Fully-autonomous miniaturized robots (e.g., drones), with artificial intelligence (AI) based visual navigation capabilities are extremely challenging drivers of Internet-of-Things edge intelligence capabilities. Visual navigation based on AI approaches, such as deep neural networks (DNNs) are becoming pervasive for standard-size drones, but are considered out of reach for nanodrones with size of a few cm2{}^\mathrm{2}. In this work, we present the first (to the best of our knowledge) demonstration of a navigation engine for autonomous nano-drones capable of closed-loop end-to-end DNN-based visual navigation. To achieve this goal we developed a complete methodology for parallel execution of complex DNNs directly on-bard of resource-constrained milliwatt-scale nodes. Our system is based on GAP8, a novel parallel ultra-low-power computing platform, and a 27 g commercial, open-source CrazyFlie 2.0 nano-quadrotor. As part of our general methodology we discuss the software mapping techniques that enable the state-of-the-art deep convolutional neural network presented in [1] to be fully executed on-board within a strict 6 fps real-time constraint with no compromise in terms of flight results, while all processing is done with only 64 mW on average. Our navigation engine is flexible and can be used to span a wide performance range: at its peak performance corner it achieves 18 fps while still consuming on average just 3.5% of the power envelope of the deployed nano-aircraft.Comment: 15 pages, 13 figures, 5 tables, 2 listings, accepted for publication in the IEEE Internet of Things Journal (IEEE IOTJ

    ENERGY-EFFICIENT AND SECURE HARDWARE FOR INTERNET OF THINGS (IoT) DEVICES

    Get PDF
    Internet of Things (IoT) is a network of devices that are connected through the Internet to exchange the data for intelligent applications. Though IoT devices provide several advantages to improve the quality of life, they also present challenges related to security. The security issues related to IoT devices include leakage of information through Differential Power Analysis (DPA) based side channel attacks, authentication, piracy, etc. DPA is a type of side-channel attack where the attacker monitors the power consumption of the device to guess the secret key stored in it. There are several countermeasures to overcome DPA attacks. However, most of the existing countermeasures consume high power which makes them not suitable to implement in power constraint devices. IoT devices are battery operated, hence it is important to investigate the methods to design energy-efficient and secure IoT devices not susceptible to DPA attacks. In this research, we have explored the usefulness of a novel computing platform called adiabatic logic, low-leakage FinFET devices and Magnetic Tunnel Junction (MTJ) Logic-in-Memory (LiM) architecture to design energy-efficient and DPA secure hardware. Further, we have also explored the usefulness of adiabatic logic in the design of energy-efficient and reliable Physically Unclonable Function (PUF) circuits to overcome the authentication and piracy issues in IoT devices. Adiabatic logic is a low-power circuit design technique to design energy-efficient hardware. Adiabatic logic has reduced dynamic switching energy loss due to the recycling of charge to the power clock. As the first contribution of this dissertation, we have proposed a novel DPA-resistant adiabatic logic family called Energy-Efficient Secure Positive Feedback Adiabatic Logic (EE-SPFAL). EE-SPFAL based circuits are energy-efficient compared to the conventional CMOS based design because of recycling the charge after every clock cycle. Further, EE-SPFAL based circuits consume uniform power irrespective of input data transition which makes them resilience against DPA attacks. Scaling of CMOS transistors have served the industry for more than 50 years in providing integrated circuits that are denser, and cheaper along with its high performance, and low power. However, scaling of the transistors leads to increase in leakage current. Increase in leakage current reduces the energy-efficiency of the computing circuits,and increases their vulnerability to DPA attack. Hence, it is important to investigate the crypto circuits in low leakage devices such as FinFET to make them energy-efficient and DPA resistant. In this dissertation, we have proposed a novel FinFET based Secure Adiabatic Logic (FinSAL) family. FinSAL based designs utilize the low-leakage FinFET device along with adiabatic logic principles to improve energy-efficiency along with its resistance against DPA attack. Recently, Magnetic Tunnel Junction (MTJ)/CMOS based Logic-in-Memory (LiM) circuits have been explored to design low-power non-volatile hardware. Some of the advantages of MTJ device include non-volatility, near-zero leakage power, high integration density and easy compatibility with CMOS devices. However, the differences in power consumption between the switching of MTJ devices increase the vulnerability of Differential Power Analysis (DPA) based side-channel attack. Further, the MTJ/CMOS hybrid logic circuits which require frequent switching of MTJs are not very energy-efficient due to the significant energy required to switch the MTJ devices. In the third contribution of this dissertation, we have investigated a novel approach of building cryptographic hardware in MTJ/CMOS circuits using Look-Up Table (LUT) based method where the data stored in MTJs are constant during the entire encryption/decryption operation. Currently, high supply voltage is required in both writing and sensing operations of hybrid MTJ/CMOS based LiM circuits which consumes a considerable amount of energy. In order to meet the power budget in low-power devices, it is important to investigate the novel design techniques to design ultra-low-power MTJ/CMOS circuits. In the fourth contribution of this dissertation, we have proposed a novel energy-efficient Secure MTJ/CMOS Logic (SMCL) family. The proposed SMCL logic family consumes uniform power irrespective of data transition in MTJ and more energy-efficient compared to the state-of-art MTJ/ CMOS designs by using charge sharing technique. The other important contribution of this dissertation is the design of reliable Physical Unclonable Function (PUF). Physically Unclonable Function (PUF) are circuits which are used to generate secret keys to avoid the piracy and device authentication problems. However, existing PUFs consume high power and they suffer from the problem of generating unreliable bits. This dissertation have addressed this issue in PUFs by designing a novel adiabatic logic based PUF. The time ramp voltages in adiabatic PUF is utilized to improve the reliability of the PUF along with its energy-efficiency. Reliability of the adiabatic logic based PUF proposed in this dissertation is tested through simulation based temperature variations and supply voltage variations
    • …
    corecore