In this paper, we investigate the suitability of magnetic tunnel junctions (MTJ) as a promising memory technology that is known for its non-volatility, swift operation, minimal power consumption, robust endurance, high density, and compatibility with vanilla CMOS. We introduce a novel in-memory computing architecture for inference of a Tsetlin machine using an MTJ crossbar for machine learning applications. In this architecture, Boolean feature inputs are represented as digital voltages, which enable parallel current paths that are determined by the MTJ resistive memory states. The resultant proportional column current is subsequently converted back into the Boolean domain for additional digital processing. Our proposed architecture reliably achieves desired inference functionality, consuming up to 9.18% less energy for MNIST and 9.08% less for F-MNIST compared to CMOS equivalents, while maintaining non-volatility, high density, and CMOS compatibility.

Edge detection is a fundamental operation in image processing, serving as a crucial step in various applications such as object recognition, image segmentation, and scene understanding. The Sobel edge detection algorithm has emerged as a widely used method for detecting vertical and horizontal edges in digital images. However, performing edge detection on high-resolution images with large dimensions can be computationally intensive and time-consuming. Specialized hardware solutions such as Field Programmable Gate Arrays (FPGAs) and Coarse-Grained Reconfigurable Arrays (CGRAs) offer significant advantages over general-purpose processors for implementing edge detection algorithms. This paper proposes algorithms for implementing the Sobel edge detection algorithm using two CGRA fabrics: dynamically reconfigurable resource array and distributed memory architecture. Furthermore, we discuss the implementation of Sobel edge detection on the target architecture for an input matrix of arbitrary size. Finally, the proposed approaches were compared with other CGRA-based implementations in terms of latency. The experimental results show that the proposed approaches exhibit significantly lower latency compared to other CGRA - based implempntations.

Energy efficiency has become the new performance criterion in this era of pervasive embedded computing; thus, accelerator-rich multi-processor system-on-chips are commonly used in embedded computing hardware. Once computationally intensive machine learning applications gained much traction, they are now deployed in many application domains due to abundant and cheaply available computational capacity. In addition, there is a growing trend toward developing hardware accelerators for machine learning applications for embedded edge devices where performance and energy efficiency are critical. Although these hardware accelerators frequently use floating-point operations for accuracy, reduced-width floating-point formats are also used to reduce hardware complexity; thus, power consumption while maintaining accuracy. Vectorization concepts can also be used to improve performance, energy efficiency, and memory bandwidth. We propose the design of a vectorized floating-point adder/subtractor that supports arbitrary length floating-point formats with varying exponent and mantissa widths in this article. In comparison to existing designs in the literature, the proposed design is 2.57× area- and 1.56× power-efficient, and it supports true vectorization with no restrictions on exponent and mantissa widths.

We introduce a novel class of massively parallel processor architectures called invasive Tightly-Coupled Processor Arrays (TCPAs). The presented processor class is a highly parameterizable template which can be tailored before runtime to fulfill costumers' requirements such as performance, area cost, and energy efficiency. These programmable accelerators are well suited for domain-specific computing from the areas of signal, image, and video processing as well as other streaming processing applications. To overcome future scaling issues (e.g., power consumption, reliability, resource management, as well as application parallelization and mapping), TCPAs are inherently designed in way that they support self-adaptivity and resource awareness at hardware level. Here, we follow a recently introduced resource-aware parallel computing paradigm called invasive computing where an application can dynamically claim, execute, and release the resources. Furthermore, we show how invasive computing can be used as an enabler for power management. For the first time, we present a seamless mapping flow for TCPAs, based on a domain-specific language. Moreover, we outline a complete symbolic mapping approach. Finally, we support our claims by comparing a TCPA against an ARM Mali-T604 GPU in terms of performance and energy efficiency.

We present a novel design methodology for the mapping of nested loops onto programmable hardware accelerators. Key features of our approach are: (1) Design entry in form of a functional programming language and loop parallelization in the polyhedron model, (2) the underlying accelerator architectures consist of lightweight, tightly-coupled, and programmable processor arrays, which can exploit both loop-level parallelism and instruction-level parallelism, (3) support of zero-overhead looping not only for inner most loops but also for arbitrarily nested loops. We implemented the proposed methodology in a prototype design tool and evaluated selected benchmarks by comparing our code generator with the Trimaran compilation framework. As the results show, our approach can reduce the size of the generated processor codes up to 64 % while at the same time achieving a significant higher throughput.

In this paper, we present an ultra low power design for a class of massively parallel architectures, called tightly-coupled processor arrays.Here, the key idea is to exploit the benefits of a decentralized resource management as inherent to invasive computing for power saving.We propose concepts and studying different architecture trade-offs for hierarchical power management by temporarily shutting down regions of processors through power gating. Moreover, a) overall system chip energy consumption, b) hardware cost, and c) timing overheads are compared for different sizes of power domains.Experimental results show that up to 70\,\% of system energy consumption may be saved for selected characteristical algorithms and different resource utilizations.

We present a self-adaptive hierarchical power management technique for massively parallel processor architectures, supporting a new resource-aware parallel computing paradigm called invasive computing. Here, an application can dynamically claim, execute, and release the resources in three phases: resource acquisition (invade), program loading/configuration and execution (infect), and release (retreat). Resource invasion is governed by dedicated decentralized hardware controllers, called invasion controllers ( i ctrls), which are integrated into each processing element (PE). Several invasion strategies for claiming linearly connected or rectangular regions of processing resources are implemented. The key idea is to exploit the decentralized resource management inherent to invasive computing for power savings by enabling applications themselves to control the power for processing resources and invasion controllers using a hierarchical power-gating approach. We propose analytical models for estimating various components of energy consumption for faster design space exploration and compare them with the results obtained from a cycle-accurate C++ simulator of the processor array. In order to find optimal design trade-offs, various parameters like (a) energy consumption, (b) hardware cost, and (c) timing overheads are compared for different sizes of power domains. Experimental results show significant energy savings (up to 73%) for selected characteristical algorithms and different resource utilizations. In addition, we demonstrate the accuracy of our proposed analytical model. Here, estimation errors less than 3.6% can be reported.