ACM Transactions on Reconfigurable Technology and Systems

Today, convolutional neural networks (CNNs) are widely used in computer vision applications. However, the trends of higher accuracy and higher resolution generate larger networks. The requirements of computation or I/O are the key bottlenecks. In this ...

Partial Reconfiguration (PR) is a key technique in the application design on modern FPGAs. However, current PR tools heavily rely on the developer to manually conduct PR module definition, floorplanning, and flow control at a low level. The existing PR ...

Recent advances in graph processing on FPGAs promise to alleviate performance bottlenecks with irregular memory access patterns. Such bottlenecks challenge performance for a growing number of important application areas like machine learning and data ...

With the trend towards ever larger “big data” applications, many of the gains achievable by using specialized compute accelerators become diminished due to the growing I/O overheads. While there have been several research efforts into computational ...

Digitizing side-channel signals at high sampling rates produces huge amounts of data, while side-channel analysis techniques only need those specific trace segments containing Cryptographic Operations (COs). For detecting these segments, waveform-matching ...

Binary neural network (BNN), where both the weight and the activation values are represented with one bit, provides an attractive alternative to deploy highly efficient deep learning inference on resource-constrained edge devices. However, our ...

Field Programmable Gate Arrays (FPGAs) have become increasingly popular in computing platforms. With recent advances in bitstream format reverse engineering, the scientific community has widely explored static FPGA security threats. For example, it is now ...

Cloud computing providers today offer access to a variety of devices, which users can rent and access remotely in a shared setting. Among these devices are SmartSSDs, which are solid-state disks (SSD) augmented with an FPGA, enabling users to instantiate ...

Stencil-based applications play an essential role in high-performance systems as they occur in numerous computational areas, such as partial differential equation solving. In this context, Iterative Stencil Loops (ISLs) represent a prominent and well-...

Lightweight PQC-related research and development have gradually gained attention from the research community recently. Ring-Binary-Learning-with-Errors (RBLWE)-based encryption scheme (RBLWE-ENC), a promising lightweight PQC based on small parameter sets ...

High-fan-in dot product computations are ubiquitous in highly relevant application domains, such as signal processing and machine learning. Particularly, the diverse set of data formats used in machine learning poses a challenge for flexible efficient ...

With the increasing application of machine learning (ML) algorithms in embedded systems, there is a rising necessity to design low-cost computer arithmetic for these resource-constrained systems. As a result, emerging models of computation, such as ...

The introduction of High Bandwidth Memory (HBM) to the FPGA chip makes it possible for an FPGA-based accelerator to leverage the huge memory bandwidth of HBM to improve its performance when implementing a specific algorithm, which is especially true for ...

Field Programmable Gate Arrays (FPGAs) are commonly used to accelerate floating-point (FP) applications. Although researchers have extensively studied FPGA FP implementations, existing work has largely focused on standalone operators and frequency-...

Emerging data-driven applications in the embedded, e-Health, and internet of things (IoT) domain require complex on-device signal analysis and data reduction to maximize energy efficiency on these energy-constrained devices. Coarse-grained reconfigurable ...

Coarse-grained reconfigurable arrays (CGRAs) are promising design choices in computation-intensive domains, since they can strike a balance between energy efficiency and flexibility. A typical CGRA comprises processing elements (PEs) that can execute ...