Coarse-grained reconfigurable architectures for radio baseband processing: A survey

Coarse-grained reconfigurable architectures (CGRAs) are gaining increasing attention as domain-specific accelerators due to their high flexibility and energy efficiency. These architectures offer a compelling solution for applications that require custom hardware performance while retaining a degree of programmability. However, the design space of CGRAs is inherently vast and complex, presenting significant challenges for architects to explore design choices efficiently and systematically. Existing design space exploration (DSE) methodologies for CGRAs are often time-demanding and struggle to deliver optimal solutions when confronted with high-dimensional and multi-objective design space. Therefore, we consider constructing a CGRA parameter tuning framework called MoDAF. MoDAF initializes the design space using the most representative and diverse samples. It adopts a divide-and-conquer approach, utilizing Monte Carlo Tree Search (MCTS) and space partitioning techniques to dynamically break down the complex design space into more manageable subspaces. A hybrid model handles local fluctuations within each subspace, while a dual sampling algorithm is designed to increase sampling efficiency. MoDAF also incorporates a fast evaluation model to estimate CGRA throughput and area, significantly speeding up the exploration process. Compared with previous approaches, experiments show that our proposed framework reduces the average distance from the reference set by 53.0% and the hypervolume deviation by 64.2%, while also cutting wall time by 57.5%.

Mobile computing as exemplified by the smart phone has become an integral part of our daily lives. The next generation of these devices will be driven by providing richer user experiences and compelling capabilities: higher definition multimedia, 3D graphics, augmented reality, and voice interfaces. To meet these goals, the core computing capabilities of mobile terminals must be scaled within highly constrained energy budgets. Coarse-grained reconfigurable architectures (CGRAs) are an appealing hardware platform for mobile systems by providing programmability with the potential for high computational throughput, low cost, and energy efficiency. CGRAs are most commonly used for innermost loops that contain an abundance of instruction-level parallelism. Unfortunately, current CGRAs fail to meet future performance requirements due to their inability to scale. Simply increasing the size of the array is too expensive in terms of power and area. In this paper, we first perform a deep analysis of several mobile applications from the domains of multimedia and gaming. We then explore potential solutions in the context of these applications for scaling the array performance in an energy efficient manner: homogeneous versus heterogeneous functionality, interconnect topologies, simple versus complex processing elements, and scalar versus vector memory support.

Coarse-grained reconfigurable architectures (CGRAs) present an appealing hardware platform by providing the potential for high computation throughput, scalability, low cost, and energy efficiency. CGRAs consist of an array of function units and register files often organized as a two dimensional grid. The most difficult challenge in deploying CGRAs is compiler scheduling technology that can efficiently map software implementations of compute intensive loops onto the array. Traditional schedulers focus on the placement of operations in time and space. With CGRAs, the challenge of placement is compounded by the need to explicitly route operands from producers to consumers. To systematically attack this problem, we take an edge-centric approach to modulo scheduling that focuses on the routing problem as its primary objective. With edge-centric modulo scheduling (EMS), placement is a by-product of the routing process, and the schedule is developed by routing each edge in the dataflow graph. Routing cost metrics provide the scheduler with a global perspective to guide selection. Experiments on a wide variety of compute-intensive loops from the multimedia domain show that EMS improves throughput by 25% over traditional iterative modulo scheduling, and achieves 98% of the throughput of simulated annealing techniques at a fraction of the compilation time.

Coarse-grained reconfigurable architectures (CGRAs) are increasingly employed as domain-specific accelerators due to their efficiency and flexibility. A CGRA typically relies on compilers to perform task scheduling. The longstanding problem of static scheduling is that it suffers from insufficient parallelism in handling irregularities due to over-serialization and workload imbalance, which leads to severe resource underutilization and performance loss. To counteract the limitations of static scheduling in CGRAs, it is essential to exploit dynamic parallelism automatically and manage hardware resources adaptively. However, existing dynamic scheduling mechanisms, e.g., work stealing, often reschedule aggressively for instant performance but sacrifice efficiency, which is unfavorable to CGRAs that emphasize efficiency and fewer reconfigurations. This article proposes an elastic task scheduling scheme that enables lightweight dynamic scheduling in CGRAs. Tasks are rescheduled at runtime according to the classic tagged-token dataflow paradigm to enable dynamic task-level parallelism. Meanwhile, tasks are dynamically resized according to run-time throughputs via duplication, combination, and substitution operators for balanced multitask execution. We implement the elastic task scheduling scheme on a well-known reconfigurable architecture - triggered instruction architecture (TIA). Evaluation on the MachSuite benchmarks shows that the proposed scheme is effective in improving performance and energy efficiency. The average speedup is 2× over the baseline. Also, our design attains a 57 percent improvement in the area-normalized performance and a 49 percent better energy efficiency. Compared with a state-of-the-art dynamic scheduling method, our scheme achieves 1.6× speedup and 1.6× energy efficiency than work-stealing mechanism on the same substrate.

Energy efficient configuration unification and compression for CGRAs

Coarse-grained reconfigurable architecture (CGRA) has become a promising candidate for data-intensive computing due to its flexibility and high energy efficiency. CGRA compilers map data flow graphs (DFGs) extracted from applications onto CGRAs, playing a fundamental role in fully exploiting hardware resources for acceleration. Yet the existing compilers are time-demanding and cannot guarantee optimal results due to the traversal search of enormous search spaces brought about by the spatio-temporal flexibility of CGRA structures and the complexity of DFGs. Inspired by the amazing progress in reinforcement learning (RL) and Monte-Carlo tree search (MCTS) for real-world problems, we consider constructing a compiler that can learn from past experiences and comprehensively understand the target DFG and CGRA.

As computation migrates to the edge, energy efficiency is becoming even more critical in the design of electronic devices. Therefore, new applications for the Internet of Things pose a challenge for designers to find the optimal balance between performance and energy consumption. While Coarse Grained Reconfigurable Architectures (CGRA) have been an alternative to provide the required acceleration, they are far from being the most energy-efficient, since their functional units may be underutilized by some applications. In this work, we introduce a novel approach to handle these CGRA resources, which leverages the variable usage of functional units due to the application program phases. By using our new approach and performing power-gating of under-utilized parts of the CGRA, we demonstrate that it is possible to achieve reductions of up to 52.3% in power dissipation and 75% in energy consumption.

Coarse-grained reconfigurable architectures (CGRAs) is one of the suitable devices for IoT (Internet of Things) and edge-computing because of their high energy efficiency and programmability. The CGRAs process a compute-intensive part of an application program (especially a loop part) more efficiently than general purpose processors. CMA (Cool Mega Array) is an energy-conscious CGRA with a task-level reconfiguration instead of a cycle-level one. However, the CMA faces some limitations related to data management because of the aggressive pursuit of power saving. In this paper, we introduce a new CMA architecture VPCMA2 to relax the constraints and to improve energy efficiency. Then, we implement it with a 65-nm process technology to evaluate a hardware overhead due to the improvement. According to the evaluation results, the new design does not influence its maximum operating frequency. Although new functionalities brought about 17% power overhead and 10% area overhead, a remarkable improvement of application mappability and data handling was achieved.

Coarse-grained reconfigurable architectures (CGRAs) provide high energy efficiency with word-level programmability rather than bit-level ones such as FPGAs. The coarser reconfigurability brings about higher energy efficiency and reduces the complexity of compiler tasks compared to the FPGAs. However, application mapping process for CGRAs is still time-consuming. When the compiler tries to map a large and complicated application data-flow-graph(DFG) onto the reconfigurable fabric, it tends to result in inefficient resource use or to fail in mapping. In case of failure, the compiler must divide it into several sub-DFGs and goes back to the same flow. In this work, we propose a novel partitioning method based on a genetic algorithm to eliminate the unmappable DFGs and improve the mapping quality. In order not to generate unmappable sub-DFGs, we also propose an estimation model which predicts the mappability and resource requirements using a DGCNN (Deep Graph Convolutional Neural Network). The genetic algorithm with this model can seek the most resource-efficient mapping without the back-end mapping process. Our model can predict the mappability with more than 98% accuracy and resource usage with a negligible error for two studied CGRAs. Besides, the proposed partitioning method demonstrates 53-75% of memory saving, 1.28-1.39x higher throughput, and better mapping quality over three comparative approaches.

Coarse-Grained Reconfigurable Architectures (CGRAs), which provide good trade-offs between performance, hardware/ energy efficiency and flexibility, as well as offer high degree of parallelism, are fast emerging as a competing platform for the high-performance and embedded applications [3]. In this paper, we propose a timing driven approach for generating cycle-accurate high level simulator for CGRA. The simulator is generated from a high-level language describing the CGRA. Experimental results on different architectures and application kernels show that the proposed simulator is \(1.5\times \) to \(4\times \) faster than state-of-the-art RTL simulators.

Coarse-grained reconfigurable architectures (CGRAs) are increasingly employed as domain-specific accelerators due to their efficiency and flexibility. However, the existing CGRA architectures suffer from low hardware resource utilization and performance due to the limitations of the scheduling scheme. In this article, an adaptive scheduler (denoted as DPTM) for concurrent and multi-task interleaved pipelining-oriented CGRA is introduced, which exploits timeslot matching and release methods to avoid the pipeline conflicts and improve the scheduling performance. The characteristics of task scheduling based on directed acyclic graph (DAG) are analyzed, and several performance-influencing factors are extracted to build a scheduling performance model for reducing the time cost of scheduling and guiding the design of scheduling schemes. Moreover, the scoreboard method of dynamic instruction schedulers is optimized to control the entry time of multiple tasks into the pipeline, and then a timeslot matching method is proposed to provide non-conflict pipelining for the multiple tasks. Further, a timeslot release method is presented to release the timeslots for unscheduled sub-tasks dynamically, which can adapt the parallel processing of multiple tasks and decrease the scheduling time. Then, an adaptive scheduling scheme combines the dynamic priority-based task assignment method, timeslot matching method, and timeslot release method to schedule massive tasks for CGRA. Finally, the overall architecture of DPTM is introduced and designed to validate the efficacy of the proposed scheduling scheme. Experimental results show that the proposed timeslot matching/release approach reduces 84% total scheduling time and decreases 40% average scheduling time at most compared to the non-timeslot-matching scheduling schemes, the proposed task assignment approach decreases 8% total scheduling time and lowers 3% average scheduling time compared to the existing approaches, and the proposed scheduler decreases 51% critical path delay, lowers 35% area overhead, and reduces 12% power consumption at most compared with the existing schedulers.

The fast evolving applications in modern digital signal processing have an increasing demand for components which have high computational power and energy efficiency without compromising the flexibility. Embedded FPGA, which is the customized FPGA with heterogeneous fine-grained application specific operations and routing resources, has shown significantly improved efficiency in terms of throughput, power dissipation and chip area for the target application domain. On the other hand, the complexity of such architecture makes it difficult to perform an efficient architecture exploration and application synthesis without tool support. In this work, we propose a framework for the design of embedded FPGA (eFPGA) architectures, which is extended from an existing framework for Coarse-Grained Reconfigurable Architectures (CGRAs). The framework is composed of a high-level modeling formalism for eFPGAs to explore the mapping space, and a retargetable application synthesis flow. To enable fast design space exploration, a force-directed placement algorithm is proposed. Finally, we demonstrate the efficacy of this framework with demanding application kernels.

Cool mega-array (CMA) is a kind of coarse grained reconfigurable architecture (CGRA) which has shown its ability of ultra low-power computation. However, as CMA completely eliminates clock trees and registers, the performance improvement has been limited. In this paper, we introduce a variable pipeline structure to CMA with the minimum essential registers to provide more wide trade-off between performance and energy. Comparing with the baseline CMA (non-pipelined structure), an average of 77% improvement for performance was achieved with a small power overhead. Moreover, the energy efficiency was 1461 MOPS / mW at most which was about 2× that of the baseline structure. The best pipeline depth for an arbitrary energy-performance trade-off became selectable with only 11% area overhead.

The dynamic run-time complexity of embedded applications is steadily increasing. Currently, only specialized Multiprocessor System-on-Chip (MPSoC) architectures can deliver the required processing power as well as energy efficiency. Although today's MPSoCs incorporate different, potentially reconfigurable cores, their ability to dynamically balance exploitable instruction-, data-, and thread-level parallelism is still very limited. In this paper, we present a novel coarse-grained reconfigurable architecture that can be adapted to operate on different computation granularities and types of parallelism at run time, depending on the application's needs. Our contributions comprise different micro architectural techniques realizing dynamic operation execution for Run-time Scalable Issue Width (RSIW) processor instances. These enable to adapt on demand the issue width of out-of-order RSIW processor instances. Our results show that significant performance improvements can be obtained by our dynamic operation execution technique compared to atomic instruction execution.

This paper presents a self adaptive architecture to enhance the energy efficiency of coarse-grained reconfigurable architectures (CGRAs). Today, platforms host multiple applications, with arbitrary inter-application communication and concurrency patterns. Each application itself can have multiple versions (implementations with different degree of parallelism) and the optimal version can only be determined at runtime. For such scenarios, traditional worst case designs and compile time mapping decisions are neither optimal nor desirable. Existing solutions to this problem employ costly dedicated hardware to configure the operating point at runtime (using DVFS). As an alternative to dedicated hardware, we propose exploiting the reconfiguration features of modern CGRAs. Our solution relies on dynamically reconfigurable isolation cells (DRICs) and autonomous parallelism, voltage, and frequency selection algorithm (APVFS). The DRICs reduce the overheads of DVFS circuitry by configuring the existing resources as isolation cells. APVFS ensures high efficiency by dynamically selecting the parallelism, voltage and frequency trio, which consumes minimum power to meet the deadlines on available resources. Simulation results using representative applications (Matrix multiplication, FIR, and FFT) showed up to 23% and 51% reduction in power and energy, respectively, compared to traditional DVFS designs. Synthesis results have confirmed significant reduction in area overheads compared to state of the art DVFS methods.