arXiv subjects

Joachim Falk

Publications and source records attributed to Joachim Falk.

4 recordsLinked to original sources

arXiv2026

Exploration of Energy and Throughput Tradeoffs for Dataflow Networks

The introduction of dynamic power management strategies such as clock gating and power gating in dataflow networks has been shown to provide significant energy savings when applied during idle times. However, these strategies can also degrade throughput due to shutdown and wake-up delays. Such throughput degradations might be particularly detrimental to signal processing systems that require a guaranteed throughput. As a solution, this paper first contributes a linear-program formulation for finding a periodic maximal-throughput schedule of a given so-called self-powering dataflow network where actors, realized in hardware, are allowed to go to sleep whenever not being enabled to fire. Depending on which actors are allowed to power down, tradeoffs between throughput and energy savings can be obtained. As a second contribution, we propose a mixed-integer-linear-program formulation to determine a periodic schedule that satisfies a given throughput while minimizing the overall energy per period by identifying a respective set of actors that is allowed to power down in phases of idleness and which rather not. Finally, as a third contribution, we propose a multi-objective design-space exploration strategy called "Hop and Skip" to efficiently explore the Pareto front of energy and throughput solutions. Experimental evaluations on a set of existing benchmarks and randomly generated graphs witness significant exploration time reductions over a brute-force sweep. Finally, a real-world case study is elaborated, and we report on achievable energy savings and throughputs of the related dataflow network where (a) all actors are always-active, (b) all actors are self-powered, and (c) all optimal energy and throughput tradeoff points as found by the proposed design-space exploration strategy.

cs.DC↗

arXiv2023

Exploring Multi-Reader Buffers and Channel Placement during Dataflow Network Mapping to Heterogeneous Many-core Systems

This paper presents an approach for reducing the memory requirements of dataflow applications, while minimizing the execution period when deployed on a many-core target. Often, straightforward implementations of dataflow applications suffer from data duplication if identical data has to be processed by multiple actors. In fact, multi-cast actors can produce huge memory overheads when storing and communicating copies of the same data. As a remedy, so-called Multi-Reader Buffers (MRBs) can be utilized to forward identical data to multiple actors in a FIFO manner while storing each data item only once. However, MRBs may increase the achievable period due to communication contention when accessing the shared data. A novel multi-objective design space exploration approach is proposed that selectively replaces multi-cast actors with MRBs and explores actor and FIFO channel mappings to find trade-offs between the objectives of period, memory footprint, and core cost. Our approach considers (i) memory-size constraints, (ii) hierarchical memories to implement the buffers, (iii) supports heterogeneous many-core platforms, and (iv) optimizes the buffer placement and overall scheduling to minimize the execution period by proposing a novel combined actor and communications scheduling heuristic for period minimization called CAPS-HMS. Our results show that the explored Pareto fronts improve a hypervolume indicator over a reference approach by up to 66 % for small to mid-size applications and 90 % for large applications. Moreover, selectively replacing multi-cast actors with corresponding MRBs proves to be always superior to never or always replacing them. Finally, it is shown that the quality of the explored Pareto fronts does not degrade when replacing the efficient scheduling heuristic CAPS-HMS by an exact integer linear programming (ILP) solver.

cs.DC↗

arXiv2019

Energy Minimization in DAG Scheduling on MPSoCs at Run-Time: Theory and Practice

Static (offline) techniques for mapping applications given by task graphs to MPSoC systems often deliver overly pessimistic and thus suboptimal results w.r.t. exploiting time slack in order to minimize the energy consumption. This holds true in particular in case computation times of tasks may be workload-dependent and becoming known only at runtime or in case of conditionally executed tasks or scenarios. This paper studies and quantitatively evaluates different classes of algorithms for scheduling periodic applications given by task graphs (i.e., DAGs) with precedence constraints and a global deadline on homogeneous MPSoCs purely at runtime on a per-instance base. We present and analyze algorithms providing provably optimal results as well as approximation algorithms with proven guarantees on the achieved energy savings. For problem instances taken from realistic embedded system benchmarks as well as synthetic scalable problems, we provide results on the computation time and quality of each algorithm to perform a) scheduling and b) voltage/speed assignments for each task at runtime. In our portfolio, we distinguish as well continuous and discrete speed (e.g., DVFS-related) assignment problems. In summary, the presented ties between theory (algorithmic complexity and optimality) and execution time analysis deliver important insights on the practical usability of the presented algorithms for runtime optimization of task scheduling and speed assignment on MPSoCs.

cs.DS↗

arXiv2019

Adaptive Predictive Power Management for Mobile LTE Devices

Reducing the energy consumption of mobile phones is a crucial design goal for cellular modem solutions for LTE and 5G standards. In addition to improving the power efficiency of components through structural and technological advances, optimizing the energy efficiency through improved dynamic power management is an integral part in contemporary hardware design. Most techniques targeting mobile devices proposed so far, however, are purely reactive in powering down and up system components. Promising approaches extend this, by predicting and using information from the environment and the communication protocol to take proactive decisions. In this paper, we propose and compare two proactive algorithmic approaches for light-weight machine learning to predict the control information needed to allow a mobile device to go to sleep states more often, e.g., in time slots of transmission inactivity in a cell. The first approach is based on supervised learning, the second one based on reinforcement learning. As the implementation of learning techniques also creates energy and resource costs, both approaches are carefully evaluated not only in terms of prediction accuracy, but also overall energy savings. Using the presented technique, we observe achievable energy savings of up to 17%.

cs.NI↗