Reliable and Energy-efficient Diabetic Retinopathy Screening using Memristor-based Neural Networks

Diabetic retinopathy (DR) is a leading cause of permanent vision loss worldwide. It refers to irreversible retinal damage caused due to elevated glucose levels and blood pressure. Regular screening for DR can facilitate its early detection and timely treatment. Neural network-based DR classifiers can be leveraged to achieve such screening in...

Lightspeed Binary Neural Networks using Optical Phase-Change Materials

This paper investigates the potential of a compute-in-memory core based on optical Phase Change Materials (oPCMs) to speed up and reduce the energy consumption of the Matrix-Matrix-Multiplication operation. The paper also proposes a new data mapping for Binary Neural Networks (BNNs) tailored for our oPCM core. The preliminary results show a...

Modeling and Analysis of SRAM PUF Bias Patterns in 14nm and 7nm FinFET Technology Nodes

SRAM Physical Unclonable Functions (PUFs) are one of the popular forms of PUFs that can be used to generate unique identifiers and randomness for security purposes. Hence, their resilience to attacks is crucial. The probability of attacks increases when the SRAM PUF start-up values follow a predictable pattern which we refer to as bias. In this...

On the Reliability of RRAM-Based Neural Networks

Emerging device technologies such as Resistive RAMs (RRAMs) are under investigation by many researchers and semiconductor companies; not only to realize e.g., embedded non-volatile memories, but also to enable energy-efficient computing making use of new data processing paradigms such as computation-in-memory. However, such devices suffer from...

Accurate and Energy-Efficient Bit-Slicing for RRAM-Based Neural Networks

Computation-in-memory (CIM) paradigm leverages emerging memory technologies such as resistive random access memories (RRAMs) to process the data within the memory itself. This alleviates the memory-processor bottleneck resulting in much higher hardware efficiency compared to von-Neumann architecture-based conventional hardware. Hence, CIM...

Severity-based Hierarchical ECG Classification using Neural Networks

Timely detection of cardiac arrhythmia characterized by abnormal heartbeats can help in the early diagnosis and treatment of cardiovascular diseases. Wearable healthcare devices typically use neural networks to provide the most convenient way of continuously monitoring heart activity for arrhythmia detection. However, it is challenging to...

A Pre-Silicon Power Leakage Assessment Based on Generative Adversarial Networks

Security is one of the most important features that a system must provide. Depending on the application of the target device, different threats should be considered at design time. However, the attack space is vast. Hence, it is difficult to decide what components to protect, what level of protection they require and how efficient they are in...

Rapid Design-Space Exploration for Low-Power Manycores under Process Variation utilizing Machine Learning

Design-space exploration for low-power manycore design is a daunting and time-consuming task which requires some complex tools and frameworks to achieve. In the presence of process variation, the problem becomes even more challenging, especially the time associated with trial-and-error selection of the proper options in the tools to obtain the...

Instruction Flow-based Detectors against Fault Injection Attacks

Fault injection attacks are a threat to all digital systems, especially to the ones conducting security sensitive operations. Recently, the strategy of observing the instruction flow to detect attacks has gained popularity. In this paper, we provide a comparative study between three hardware-based techniques (i.e., recurrent neural network (RNN)...

Improving the Security of the IEEE 802.15.6 Standard for Medical BANs

A Medical Body Area Network (MBAN) is an ensemble of collaborating, potentially heterogeneous, medical devices located inside, on the surface of or around the human body with the objective of tackling one or multiple medical conditions of the MBAN host. These devices-which are a special category of Wireless Body Area Networks (WBANs)–collect,...

CIM-based Robust Logic Accelerator using 28 nm STT-MRAM Characterization Chip Tape-out

Spin-transfer torque magnetic random access memory (STT-MRAM) based computation-in-memory (CIM) architectures have shown great prospects for an energy-efficient computing. However, device variations and non-idealities narrow down the sensing margin that severely impacts the computing accuracy. In this work, we propose an adaptive referencing...

Smart Redundancy Schemes for ANNs against Fault Attacks

Artificial neural networks (ANNs) are used to accomplish a variety of tasks, including safety critical ones. Hence, it is important to protect them against faults that can influence decisions during operation. In this paper, we propose smart and low-cost redundancy schemes that protect the most vulnerable ANN parts against fault attacks....

Deterministic and Statistical Strategies to Protect ANNs against Fault Injection Attacks

Artificial neural networks are currently used for many tasks, including safety critical ones such as automated driving. Hence, it is very important to protect them against faults and fault attacks. In this work, we propose two fault injection attack detection mechanisms: one based on using output labels for a reference input, and the other on...

Guard-NoC: A protection against side-channel attacks for MPSoCs

Multi-Processor System-on-Chips (MPSoCs) are popular computational platforms for a wide variety of applications due to their energy efficiency and flexibility. Like many other platforms they are vulnerable to Side Channel Attacks (SCAs). In particular, Logical SCAs (LSCAs) are very powerful as sensitive information can be retrieved by simply...