This article proposes an event-driven solution to genotype imputation, a technique used to statistically infer missing genetic markers in DNA. The work implements the widely accepted Li and Stephens model, primary contributor to the computational complexity of modern x86 solutions, in an attempt to determine whether further investigation of the application is warranted in the event-driven domain. The model is implemented using graph-based Hidden Markov Modeling and executed as a customized forward/backward dynamic programming algorithm. The solution uses an event-driven paradigm to map the algorithm to thousands of concurrent cores, where events are small messages that carry both control and data within the algorithm. The design of a single processing element is discussed. This is then extended across multiple cores and executed on a custom RISC-V NoC cluster called POETS. Results demonstrate how the algorithm scales over increasing hardware resources and a multi-core run demonstrates a 270X reduction in wall-clock processing time when compared to a single-threaded x86 solution. Optimisation of the algorithm via linear interpolation is then introduced and tested, with results demonstrating a wall-clock reduction time of ∼ 5 orders of magnitude when compared to a similarly optimised x86 solution.

Inference at-the-edge using embedded machine learning models is associated with challenging trade-offs between resource metrics, such as energy and memory footprint, and the performance metrics, such as computation time and accuracy. In this work, we go beyond the conventional Neural Network based approaches to explore Tsetlin Machine (TM), an emerging machine learning algorithm, that uses learning automata to create propositional logic for classification. We use algorithm-hardware co-design to propose a novel methodology for training and inference of TM. The methodology, called REDRESS, comprises independent TM training and inference techniques to reduce the memory footprint of the resulting automata to target low and ultra-low power applications. The array of Tsetlin Automata (TA) holds learned information in the binary form as bits: {0,1}, called excludes and includes, respectively. REDRESS proposes a lossless TA compression method, called the include-encoding, that stores only the information associated with includes to achieve over 99% compression. This is enabled by a novel computationally minimal training procedure, called the Tsetlin Automata Re-profiling, to improve the accuracy and increase the sparsity of TA to reduce the number of includes, hence, the memory footprint. Finally, REDRESS includes an inherently bit-parallel inference algorithm that operates on the optimally trained TA in the compressed domain, that does not require decompression during runtime, to obtain high speedups when compared with the state-of-the-art Binary Neural Network (BNN) models. In this work, we demonstrate that using REDRESS approach, TM outperforms BNN models on all design metrics for five benchmark datasets viz. MNIST, CIFAR2, KWS6, Fashion-MNIST and Kuzushiji-MNIST. When implemented on an STM32F746G-DISCO microcontroller, REDRESS obtained speedups and energy savings ranging 5-5700× compared with different BNN models.

Performance and energy efficiency considerations have shifted computing paradigms from single-core to many-core architectures. At the same time, traditional speedup models such as Amdahl's Law face challenges in the run-time reasoning for system performance and energy efficiency, because these models typically assume limited variations of the parallel fraction. Moreover, the parallel fraction, which varies dynamically in workloads, is generally unknown at run-time without application-level instrumentation. This article describes novel performance/energy trade-off models based on realistic architectural considerations, which describe the parallel fraction and speedup as functions of performance counter values available in modern processors, removing the need for application-level instrumentation. These are then used to develop a Parallelization-Aware Run-time Management (PARMA) approach. PARMA aims at controlling core allocations and operating voltage/frequency points for energy efficiency, according to the varying workload parallel fractions. The efficacy of our models and the PARMA approach is extensively validated using a number of PARSEC benchmark applications, involving two performance/energy trade-off metrics: energy-delay-product (EDP), typically used in high-performance applications and energy per instruction (EPI), suitable for energy-aware applications. Up to 48 and 68 percent improvements in EDP and EPI have been observed using the PARMA approach compared with parallelization-agnostic methods.

This paper describes the challenges and lessons learned in the experience-centered design (ECD) of the Spheres of Wellbeing, a technology to promote the mental health and wellbeing of a group of women, suffering from significant mental health problems and living in a medium secure hospital unit. First, we describe how our relationship with mental health professionals at the hospital and the aspirations for person-centric care that we shared with them enabled us, in the design of the Spheres, to innovate outside traditional healthcare procedures. We then provide insights into the challenges presented by the particular care culture and existing services and practices in the secure hospital unit that were revealed through our technology deployment. In discussing these challenges, our design enquiry opens up a space to make sense of experience living with complex mental health conditions in highly constrained contexts within which the deployment of the Spheres becomes an opportunity to think about wellbeing in similar contexts.

With the increase of system complexity in both platforms and applications, power modelling of heterogeneous systems is facing grand challenges from the model scalability issue. To address these challenges, this paper studies two systematic methods: selective abstraction and stochastic techniques. The concept of selective abstraction via black-boxing is realised using hierarchical modelling and cross-layer cuts, respecting the concepts of boxability and error contamination. The stochastic aspect is formally underpinned by Stochastic Activity Networks (SANs). The proposed method is validated with experimental results from Odroid XU3 heterogeneous 8-core platform and is demonstrated to maintain high accuracy while improving scalability.

Modern embedded systems execute multiple applications, both sequentially and concurrently. These applications are exercised on heterogeneous platforms generating varying power consumption and system workloads (CPU or memory intensive or both). As a result, determining the most energy-efficient system configuration (i.e. the number of parallel threads, their core allocations and operating frequencies) tailored for each kind of workload and application scenario is extremely challenging. In this paper, we propose a novel runtime optimization approach with the aim of achieving maximized power normalized performance considering dynamic variation of workload and application scenarios. Fundamental to this approach is a comprehensive study to investigate the tradeoffs between inter-application concurrency with performance and power consumption under different system configurations. Using real experimental measurements on an Odroid XU-3 heterogeneous platform with a number of PARSEC benchmark applications, we model power normalized performance (in terms of IPS/Watt) underpinning analytical power and performance models, derived through multivariate linear regression (MLR). Using these models, we show that with increasing number of concurrent CPU intensive applications show variable gains in IPS/Watt compared to the memory intensive applications in both sequential and concurrent application scenarios. Furthermore, we demonstrate that it is possible to continuously adapt system configuration through a low-cost and linear-complexity runtime algorithm, which can improve the IPS/Watt by up to 125% compared to the existing approach.

We relate the experience of a defining a formal domain specific language (DSL) for the construction and reasoning about OS-level management logic of multi-core systems. The approach is based on a novel, iterative development principle where results of prototyping studies feed back into the next language revision. We illustrate the DSL with several examples of executable scripts.

The choice of radix is crucial for multivalued logic synthesis. Practical examples, however, reveal that it is not always possible to find the optimal radix when taking into consideration actual physical parameters of multivalued operations. In other words, each radix has its advantages and disadvantages. Our proposal is to synthesize logic in different radices, so it may benefit from their combination. The theory presented in this paper is based on Reed-Muller expansions over Galois field arithmetic. The work aims to first estimate the potential of the new approach and to second analyze its impact on circuit parameters down to the level of physical gates. The presented theory has been applied to real-life examples focusing on cryptographic circuits where Galois Fields find frequent application. The benchmark results show that the approach creates a new dimension for the trade-off between circuit parameters and provides information on how the implemented functions are related to different radices.

A conversion driven design approach is described. It takes the outputs of mature and time-proven EDA synthesis tools to generate mixed radix datapath circuits in an endeavour to investigate the added relative advantages or disadvantages. An algorithm underpinning the approach is presented and formally described together with m-of-n encoded gate-level implementations. The application is found in a wide variety and overlapping areas of circuit design, here a subset are analysed where the method finds the strongest application: arithmetic circuits and hardware security. The obtained results are reported showing an increase in power consumption but with considerable improvement in resistance to differential power analysis (DPA).