The growing trend of pervasive computing has consolidated the everlasting need for power efficient devices. The conventional cache subsystem of general-purpose CPUs, while being able to adapt to many use cases, suffers from energy inefficiencies in some scenarios. It is well-known by now in the academic literature that the utilization of a scratchpad memory (SPM) can help reducing the overall energy consumption of embedded systems. This work proposes a hybrid cache-SPM architecture with support logic for semi-transparent data management and spatial locality improvement. Selected data are transferred and stored in the SPM in a compact form using dynamic layout transformation. As a second major contribution, we introduce a methodology to identify memory access sequences that make an inefficient use of the cache, marking them as candidates to be moved to an SPM of constrained space. The methodology does not require access to the source code of the target applications, relying on binary instrumentation and offline profiling. The resulting mapping policies have been tested on a simulated system, showing a mean memory dynamic energy reduction of 43% and a mean speed gain of 13% with a representative benchmark set.

Electromigration (EM) has been known as a reliability threatening factor for back-end-of-the-line interconnects. Spin-transfer torque magnetic RAM (STT-MRAM) is an emerging nonvolatile memory that has gained a lot of attention in recent years. However, relatively large operational current magnitude is a challenge for this technology, and hence, EM can be a potential reliability concern, even for the signal lines of this memory. A workload-aware EM modeling needs to capture time-dependent current density in the memory signal lines and to be able to predict the effect of the EM phenomenon on the interconnect for its entire lifetime. In this work, we present methods to effectively model the workload-dependent EM-induced meantime to failure (MTTF) in typical STT-MRAM arrays under a variety of realistic workloads. This allows performing the design-space exploration to co-optimize reliability and other design metrics.

As the technology scaling advances, limitations of traditional memories in terms of density and energy become more evident. Modern caches occupy a large part of a CPU physical size and high static leakage poses a limit to the overall efficiency of the systems, including IoT/edge devices. Several alternatives to CMOS SRAM memories have been studied during the past few decades, some of which already represent a viable replacement for different levels of the cache hierarchy. One of the most promising technologies is the spin-transfer torque magnetic RAM (STT-MRAM), due to its small basic cell design, almost absent static current and non-volatility as an added value. However, nothing comes for free, and designers will have to deal with other limitations, such as the higher latencies and dynamic energy consumption for write operations compared to reads. The goal of this work is to explore several microarchitectural parameters that may overcome some of those drawbacks when using STT-MRAM as last-level cache (LLC) in embedded devices. Such parameters include: number of cache banks, number of miss status handling registers (MSHRs) and write buffer entries, presence of hardware prefetchers. We show that an effective tuning of those parameters may virtually remove any performance loss while saving more than 60% of the LLC energy on average. The analysis is then extended comparing the energy results from calibrated technology models with data obtained with freely available tools, highlighting the importance of using accurate models for architectural exploration.

RISC-V FPGA, also written RVfpga, is a set of two freely available courses developed by the authors and Imagination Technologies that enable users to understand and use the RISC-V instruction set architecture (ISA), a commercial RISC-V core and system, and the RISC-V ecosystem. The first course, RVfpga, includes comprehensive instructions, tools, and labs for targeting a commercial RISC-V processor to a field programmable gate array (FPGA) and then using and expanding it to learn about computer architecture, digital design, embedded systems, system-on-chip (SoC) design, and programming. The topics covered include targeting the RISC-V SoC to an FPGA, programming in C and RISC-V assembly, running programs in simulation or, optionally, in hardware, using peripherals and adding new ones to the SoC, and analyzing and modifying the RISC-V core and memory system, including adding new instructions to the core. The follow-on course, RVfpga-SoC, shows how to build a RISC-V SoC from building blocks and then run the Zephyr real-time operating system (RTOS) on it. At the completion of these courses, users will have a working RISC-V system and have hands-on experience exploring and using both the RISC-V SoC and the RISC-V toolchain, including compilers and simulators.

Spin Transfer Torque Magnetic RAM (STT-MRAM) is being extensively considered as a promising replacement for Last Level Caches (LLC), due to its high density, low leakage and non-volatility. However, writes to STT-MRAM are energy intensive and have a high latency. While the high dynamic energy consumption during writes can be compensated by the low static energy consumption, the high latency results in performance degradation. This work shows that in contrast to SRAM-based LLCs, the performance degradation for STT-MRAM is primarily due to bank contention, when trying to satisfy a read request while the bank is being written. We holistically explore the effects of cache banking and cache contention on energy and performance in the LLC of mobile multicore systems, with in-order cores or with out-of-order cores. The detail of the analysis is enabled by highly accurate cache models, based on a 28nm SRAM industry compiler, and an in-house developed STT-MRAM compiler, which generates full STT-MRAM macro designs with silicon-validated MTJ stack and complete parasitic extraction at the 28nm node. Our results show that there is a clear difference in the energy-performance optimal banking configuration between STT-MRAM caches and SRAM caches. These low contention STT-MRAM cache designs with the optimal number of banks save at least 60% cache energy while losing at most single digit percentages in system performance compared to SRAM cache designs. This show an increased potential of using STT-MRAM as a replacement for SRAM in an LLC.

Current main memory organizations in embedded and mobile application systems are DRAM dominated. The ever-increasing gap between today's processor and memory speeds makes the DRAM subsystem design a major aspect of computer system design. However, the limitations to DRAM scaling and other challenges like refresh provide undesired trade-offs between performance, energy and area to be made by architecture designers. Several emerging NVM options are being explored to at least partly remedy this but today it is very hard to assess the viability of these proposals because the simulations are not fully based on realistic assumptions on the NVM memory technologies and on the system architecture level. In this paper, we propose to use realistic, calibrated STT-MRAM models and a well calibrated cross-layer simulation and exploration framework, named SEAT, to better consider technologies aspects and architecture constraints. We will focus on general purpose/mobile SoC multi-core architectures. We will highlight results for a number of relevant benchmarks, representatives of numerous applications based on actual system architecture. The most energy efficient STT-MRAM based main memory proposal provides an average energy consumption reduction of 27% at the cost of 2x the area and the least energy efficient STT-MRAM based main memory proposal provides an average energy consumption reduction of 8% at the around the same area or lesser when compared to DRAM.

STT-MRAM (Spin Transfer Torque Magnetic Random Access Memory) has attracted considerable attention of late since it is the most promising logic compatible nonvolatile memory that is suitable for advanced logic nodes (N28 and beyond) in terms of endurance, speed and power. Embedded STT-MRAM has thus been proposed as a candidate for emerging low standby-power connectivity systems such IoT (Internet-of-Things) and wearables. We utilize the high performance CoFeB based perpendicular MTJ (pMTJ) device to realize a low power and highly dense STT-MRAM array for such systems. This study is carried out on the TSMC 28nm technology node and includes a complete cross-layer design and analysis framework ranging from device modeling to circuit design, layout and system implementation. The process variations and temperature (PT) impact on the MTJ for the STT-MRAM design (and correspondingly the total energy consumption and performance of the system) is also analyzed. We report a ∼85% reduction in the energy consumption compared to the baseline SRAM based system for near negligible performance penalty (<5%).

SRAM based memory systems are plagued by a number of problems like sub-threshold leakage and susceptibility to read/write failure with dynamic voltage scaling schemes or low supply voltage. Non-Volatile Memory (NVM) technologies are being explored extensively nowadays to replace the conventional SRAM memories even for level 1 (L1) caches. These NVMs like Spin Torque Transfer RAM (STT-MRAM), Resistive-RAM (ReRAM) and Phase Change RAM (PRAM) are less hindered by leakage problems with technology scaling and consume lesser area. However, simple replacement of SRAM by NVMs is not a viable option due to their write related issues. The main focus of this paper is the exploration of write delay and write energy issues in a NVM based L1 Instruction cache (I-cache) for an ARM like single core system. We propose a NVM I-cache and extend its MSHR (Miss Status Handling Register) functionality to address the NVMs write related issues. According to our simulations, appropriate tuning of selective architecture parameters can reduce the performance penalty introduced by the NVM (∼45%) to extremely tolerable levels (∼1%) and show energy gains up to 35%. Furthermore, on configuring our modified NVM based system to occupy area comparable to the original SRAM-based configuration, it outperforms the SRAM baseline and leads to even more energy savings.

This article addresses the compilation of a sequential program for parallel execution on a modern GPU. To this end, we present a novel source-to-source compiler called PPCG. PPCG singles out for its ability to accelerate computations from any static control loop nest, generating multiple CUDA kernels when necessary. We introduce a multilevel tiling strategy and a code generation scheme for the parallelization and locality optimization of imperfectly nested loops, managing memory and exposing concurrency according to the constraints of modern GPUs. We evaluate our algorithms and tool on the entire PolyBench suite.

Process variability and dynamic domains increase the uncertainty of embedded systems and force designers to apply pessimistic designs, which become unnecessarily conservative and have a tremendous impact on both performance and energy consumption. In this context, developing uncertainty-aware design methodologies that take both variation at platform and at application level into account becomes a must. These methodologies should mitigate the effects derived from uncertainty, avoiding worst-case assumptions. In this article we propose a comprehensive methodology to tackle two forms of uncertainty: (1) process variation on the memory system, (2) application dynamism. A statistical model has been developed to deal with variability derived from fabrication process, whereas system scenarios are selected to cope with dynamic domains. Both sources of uncertainty are firstly tackled in combination at design time, to be refined later, at setup. As a result, at run time the platform can be successfully adapted to the current application behaviour as well as the current variations. Our simulations show that this methodology provides significant energy savings while still meeting strict timing constraints.

Similarity search has been widely studied in the last years, as it can be applied to several fields such as searching by content in multimedia objects, text retrieval or computational biology. These applications usually work on very large databases that are often indexed off-line to enable the acceleration of on-line searches. However, to maintain an acceptable throughput, it is essential to exploit the intrinsic parallelism of the algorithms used for the on-line query solving process, even with indexed databases. Therefore, many strategies have been proposed in the literature to parallelize these algorithms, both on shared and distributed memory multiprocessor systems. Lately, GPUs have also been used to implement brute-force approaches instead of using indexing structures, due to the difficulties introduced by the index in the efficient exploitation of the GPU resources. In this work we propose a Multi-GPU metric-space technique that efficiently exploits index data structures for similarity search in large databases, and show how it outperforms previous OpenMP and GPU brute-force strategies. Furthermore, our analysis covers the effects of the database size and its nature.

Interactive Response Systems (IRS) have been gaining acceptance within the educational community in recent years and a clear proof is the growing number of commercial systems available today in the market. However, most solutions are based on systems which are closed, rigid and dependent on proprietary keypad or platform. We have developed OpenIRS-UCM, a free teaching tool for interactive polling that solves these drawbacks. It is an open source software so it allows the development of new functions by anybody. It has a friendly interface that anyone without high computer skills can use. It enables the coexistence of several commercial clickers simultaneously with smart-phones, tablets or other modern electronic devices. It is developed in Java, thus its use is not restricted to systems based on Microsoft Windows and it is independent of any proprietary software.

A great interest has been given to the Nonnegative Matrix Factorization (NMF) technique due to its ability of extracting highly-interpretable parts from data sets. Gene expression analysis is one of the most popular applications of NMF in Bioinformatics. Nonetheless, its usage is hindered by the computational complexity when processing large data sets. In this paper, we present two parallel implementations of NMF. The first version uses CUDA on a Graphics Processing Unit (GPU). Large input matrices are iteratively blockwise transferred and processed. The second implementation distributes data among multiple GPUs synchronized through MPI (Message Passing Interface). When analyzing large data sets with two and four GPUs, it performs respectively, 2.3 and 4.13 times faster than the single-GPU version. This represents about 120 times faster than a conventional CPU. These super linear speedups are achieved when data portions assigned to each GPU are small enough to be transferred only once.

Process variation and the dynamism of modern applications can degrade the expected performance of a system. Execution time can be severely affected by both factors, resulting in deadline violations and energy consumption overheads. Memory organizations, which account for a large part of the system energy and the time budgets, are especially vulnerable to process variation. Configurable - multimode - memories are a promising technology to deal with these problems, but they also introduce new issues that need to be solved. Essentially, adding configuration capabilities to the memories comes with a cost, both in memory area and control complexity; hence, we need to evaluate what is the minimum amount of re-configurability to satisfy system's constraints. In this paper, we analyze the scalability of configurable memories and highlight the relationship among mode allocation, memory mapping and data allocation.

Current operating system schedulers are not fully aware of multi-core and multi-threaded architectures, and as a result, schedule threads in a way that may cause contention for critical resources such as the last level in the cache memory hierarchy or the memory access bandwidth. This contention has a significant impact on the system productivity and the quality of service that each individual thread gets from the platform, which can widely vary depending on the behavior of its simultaneous co-runners.In this paper we describe the design and implementation of a non-work-conserving framework to schedule threads that tries to improve priority enforcement, based on on-line statistics collected through hardware performance counters. We have implemented our scheme in Linux running on both multicore and SMT processors. For synthetic workloads based on the latest SPEC CPU2006 benchmarks, our framework speeds up high-priority threads by up to 50%, while keeping or even slightly improving the overall system throughput.

Process variability and the dynamism of new applications increase the uncertainty of embedded systems and force designers to use pessimistic assumptions, which have a tremendous impact on both the performance and energy consumption of their memory organizations. In this article we introduce an experimental framework which tries to mitigate the effects of both sources of unpredictability. At compile time, an extensive profiling helps us to detect system scenarios and bounds application dynamism. At the organization level, we incorporate a heterogeneous memory architecture composed by several configurable memories. A calibration process and a runtime control system adapt the platform to the current application needs. Our approach manages to reduce significantly the energy overhead associated to both variability and application dynamism (up to 60%, according to our simulations) without compromising the timing constraints existing in our target domain of dynamic periodic multimedia applications.