Processing In-Memory (PIM) is a data-centric computation paradigm that performs computations inside the memory, hence eliminating the memory wall problem in traditional computational paradigms used in Von-Neumann architectures. The associative processor, ...

With the increasing complexity of cyber-physical systems (CPS), it is interesting to decompose a CPS into sub-systems. This provides greater modularity and flexibility so that each system can be developed independently, making it easier to maintain. Also, ...

In this work mapping of quantum circuits to regular hexagonal grid with coupling degree of six has been investigated. Architectures involving superconducting qubits impose restrictions on 2-qubit gate operations to be carried out only between physically ...

DRAM memory controller plays a critical role in maximizing the performance of high bandwidth memory by efficiently managing data transfers between the CPU and the memory modules. Thus, they are suitable for low-power data-intensive applications. However, ...

Rapid advancements in edge devices have led to a large deployment of deep neural network (DNN) based workloads. To utilize the resources at the edge effectively, many DNN compilers are proposed that efficiently map the high level DNN models developed in ...

Due to the advancement of transistor technology, a single chip processor can now have hundreds of cores. Network-on-Chip (NoC) has been the superior interconnect fabric for multi/many-core on-chip systems because of its scalability and parallelism. Due to ...

Fluidic automation, the practice of programmatically manipulating small fluids to execute laboratory protocols, has led to vastly increased productivity for biologists and chemists. Most fluidic programs, commonly referred to as protocols, are written ...

Many commercially available memory chips are fabricated worldwide in untrusted facilities. Therefore, a counterfeit memory chip can easily enter into the supply chain in different formats. Deploying these counterfeit memory chips into an electronic system ...

There is an increasing demand for running neural network inference on edge devices. Memristor crossbar array (MCA) based accelerators can be used to accelerate neural networks on edge devices. However, reliability issues in memristors, such as stuck-at ...

Multi-tenant applications have been proliferating in recent years, supported by the emergence of computing-as-service paradigms. Unfortunately, multi-tenancy induces new security vulnerabilities due to spatial or temporal co-location of applications with ...

We propose AccHashtag, the first framework for high-accuracy detection of fault-injection attacks on Deep Neural Networks (DNNs) with provable bounds on detection performance. Recent literature in fault-injection attacks shows the severe DNN accuracy ...

The acceleration of Deep Neural Networks (DNNs) has attracted much attention in research. Many critical real-time applications benefit from DNN accelerators but are limited by their compute-intensive nature. This work introduces an accelerator for ...

Convolutional neural networks (CNNs) have gained a massive impression in the fields of computer vision and especially in the embedded applications because of their high accuracy and performance. However, high computational complexity and power consumption ...

Conventional electronic memory hierarchies are intrinsically limited in their ability to overcome the memory wall due to scaling constraints. Optical caches and interconnects can mitigate these constraints, and enable processors to reach performance and ...

Today’s intermittent computing systems operate by relying only on harvested energy accumulated in their tiny energy reservoirs, typically capacitors. An intermittent device dies due to a power failure when there is no energy in its capacitor and boots ...

This article presents Computational SRAM (C-SRAM) solution combining In- and Near-Memory Computing approaches. It allows performing arithmetic, logic, and complex memory operations inside or next to the memory without transferring data over the system bus,...

Conventional von Neumann architectures cannot successfully meet the demands of emerging computation and data-intensive applications. These shortcomings can be improved by embracing new architectural paradigms using emerging technologies. In particular, ...