CGRAs: Reconfigurable Arrays in Accelerator Design

Updated 3 December 2025

CGRAs are reconfigurable hardware architectures comprising a 2D array of processing elements that execute arithmetic and logic operations, bridging CPUs and ASICs.
They optimize energy, latency, and throughput through advanced mapping, scheduling, and power-timing simulation methodologies.
Innovations such as SAT-based mapping, dynamic reconfiguration, and approximate arithmetic enable enhanced performance and efficiency.

Coarse-Grain Reconfigurable Arrays (CGRAs) represent a class of reconfigurable hardware architectures situated between traditional CPUs and more specialized options such as FPGAs or ASICs. CGRAs comprise a 2D array of processing elements (PEs), tightly integrated to execute arithmetic and logic operations, offering strong programmability with ASIC-like performance. The highly variable implementation space, encompassing diverse topologies, interconnects, and functional unit configurations, results in significant challenges for efficient mapping, scheduling, and exploring trade-offs in energy, latency, and throughput.

1. CGRA Architectural Principles and Methodologies

CGRAs are structured as two-dimensional arrays of processing elements, each supporting arithmetic and logic operations. Each PE is typically equipped with a register file, an arithmetic logic unit, and local memory interfaces. CGRA topologies vary, encompassing mesh, ring, bus, and NoC-based interconnects. For example, researchers propose integrated modules (kernel simulator, power and timing estimator) to simulate kernel execution behavior, track PE state transitions, and model neighbor inputs and memory I/O events. The control plane is abstracted via instruction sets simulated per cycle, achieving functional accuracy thousands of cycles per second (Aspros et al., 2 Apr 2025).

Advanced CGRA microarchitectures, such as R-Block disaggregated tiles, can integrate both accurate and approximate functional units, local memory, SIMD support, and customized interconnects (Wilton switchboxes), enabling higher degrees of parallelism. Circuit-level innovations facilitate domain partitioning for voltage islands, exploiting inherent slack to optimize power (Alexandris et al., 29 May 2025).

2. Power and Timing Modeling Techniques

Energy and latency estimation in CGRAs traditionally requires post-synthesis simulation. Modern frameworks decouple these using cycle-accurate behavioral simulation paired with hierarchical power models. Dynamic power is calculated per operation by $P_{\mathrm{dyn}} = \alpha \cdot C \cdot V^2 \cdot f$ , where $\alpha$ is a profile-derived activity factor, $C$ switched capacitance, $V$ the supply voltage, and $f$ clock frequency. Static dissipation is modeled as $P_{\mathrm{stat}} = I_{\mathrm{leak}} \cdot V$ , corresponding to leakage during idle or barrier waits (Aspros et al., 2 Apr 2025).

Latency is mapped by:

$T_{\mathrm{latency}} = \sum_{i} \frac{\mathrm{cycles}_i}{f_{\mathrm{clk}}}$

where $\mathrm{cycles}_i$ include per-op latencies, optional stalls due to memory or PE contention, and globally synchronized micro-operation completion. Simulation frameworks can inject non-idealities incrementally, achieving sub-25% mean error compared to post-synthesis "ground truth".

Such tools support instantaneous Pareto analysis for energy-latency scheduling strategies, and reporting per-instruction, per-PE heatmaps, which illuminate bottlenecks such as static power dominance during memory-heavy cycles.

3. Advanced Software Mapping: Scheduling, Allocation, and SAT-Based Formulations

Efficient mapping of loops or dataflow graphs (DFG) onto CGRA fabrics is combinatorially challenging. Modulo scheduling divides loop execution into prologue, kernel (with steady-state repeat interval II), and epilogue. Minimizing II is crucial for throughput. The theoretical lower bound is:

$II \geq \max \{\mathrm{ResMII},\, \mathrm{RecMII}\}$

with $\mathrm{ResMII} = \lceil \text{\#ops}/\text{\#PEs} \rceil$ as the resource bound and $\mathrm{RecMII}$ as the maximal recurrence bound.

The Kernel Mobility Schedule (KMS) encodes all admissible assignments for a fixed II, folding ASAP/ALAP time windows modulo II, and is central to SAT-based mapping techniques. Boolean variables $x_{n,p,c,it}$ denote placement of DFG node $n$ on PE $p$ at cycle $c$ of iteration $it$ , with constraints ensuring uniqueness, resource bounds, temporal and spatial data dependencies, and feasible routing (Tirelli et al., 2 Dec 2025, Tirelli et al., 2 Dec 2025). SAT-MapIt and similar frameworks iteratively search II and optimize the mapping, achieving the minimum feasible II and competitive compilation times.

Monomorphism-based mapping further decouples time and space, using SMT for schedule and fast subgraph isomorphism for spatial embedding. This decoupling preserves mapping optimality while yielding dramatic compile-time speedups ( $10^5 \times$ on large arrays) (Tirelli et al., 2 Dec 2025).

4. Time-Multiplexing, Resource Arbitration, and Multi-Task Support

CGRAs support both spatial and time-multiplexed execution. In time-multiplexed mode, PEs execute context-dependent micro-operations per cycle, scheduled to optimize overall resource usage. Resource arbitration handles contention for shared memory/bus accesses, with benchmarks showing significant latency and energy improvements when employing advanced arbitration schemes and adaptable scheduling (Aspros et al., 2 Apr 2025).

Support for multi-task execution uses partitioned hardware abstractions—in particular, GLB-slices (global buffer) and array slices—enabling flexible-shape execution regions decoupled from rigid aspect ratios. Dynamic partial reconfiguration (DPR) mechanisms allow rapid bitstream swapping, reducing reconfiguration overhead to under 5% of total frame latency, thus enabling real-time or cloud-like task multiplexing with up to $1.24 \times$ throughput and $28\%$ latency reduction (Kong et al., 2023).

5. Integration of Approximate Arithmetic and Physical Optimization

Recent frameworks introduce heterogeneous processing elements that mix accurate and approximate arithmetic units (such as DRUM-k multipliers for INT8 convolution), mapped by per-channel accuracy-degradation constraints. ConvNets with channel-wise exploration optimize energy savings while bounding output error, using importance factors to drive the mapping:

$\min \sum_{\ell, oc} E_{\mathrm{saved}}(oc, \ell) \cdot x_{oc, \ell} \quad \text{subject to} \quad \sum_{\ell, oc} I_{oc, \ell} \cdot x_{oc, \ell} \leq \epsilon_{\max}$

Partitioning voltage domains according to critical path slack reduces dynamic power by nearly $30\%$ with minimal ( $2\%$ ) area overhead, supporting best-in-class energy efficiency across competitive benchmarks ($440$ GOPS/W) (Alexandris et al., 29 May 2025).

6. Insights from Benchmarking, Toolchains, and Practical Evaluations

Comparative analysis of four CGRA mapping toolchains reveals that multi-hop, single-cycle interconnects (e.g., HyCUBE) significantly reduce initiation intervals and help alleviate underutilization of resources. Despite advanced mapping, typical utilization remains below $60\%$ due to routing constraints and workload structure. The need for nested loop mapping and predication in DFGs is essential for practical deployability (Walter et al., 26 Feb 2025).

Benchmarks spanning MiBench, Rodinia, and PolyBench demonstrate that integrated power-timing simulation frameworks, advanced compilation scheduling, and resource abstraction provide reliable architectural exploration in seconds rather than days, with average error rates well suited for design-space pruning (Aspros et al., 2 Apr 2025). Integration of motif-based dataflow decomposition (as in Plaid CGRA) further balances compute and communication provisioning, enabling $43\%$ power and $46\%$ area savings at maintained throughput (Li et al., 11 Dec 2024).

7. Future Directions: Flexible Toolchains, Co-Design Frameworks, and Scalability

Emerging domains, such as low-power edge AI and transformer inference, drive CGRA innovation towards modular, open-source, and co-design paradigms. Multi-agent LLM-driven co-design frameworks automate HW/SW optimization, iterating over PPA objectives (performance, power, area) and rapidly narrowing the design space with stateful feedback mechanisms (Jiang et al., 16 Sep 2025). Agile, unified abstraction layers (e.g., VS-IR) decouple software mapping from physical architecture, fostering reproducibility and scalable design exploration (Juneja et al., 26 Aug 2025).

Extensions to dynamic reconfiguration, hierarchical mapping, runahead memory systems, approximate arithmetic with fine-grained voltage domains, and compiler-guided cache reconfiguration strategies promise further exploitation of spatial and temporal parallelism, reduced area/power, and resilience to aging or process variation (Aspros et al., 2 Apr 2025, Brandalero et al., 2020, Liu et al., 13 Aug 2025).

Summary Table: Core CGRA Concepts and Innovations

Aspect	Approach/Metric	Reference
Architecture	2D PE array, time-multiplexed	(Aspros et al., 2 Apr 2025)
Power Modeling	$P_{\mathrm{dyn}} = \alpha C V^2 f$ , $P_{\mathrm{stat}} = I_{\mathrm{leak}} V$	(Aspros et al., 2 Apr 2025)
Scheduling/Mobility	Kernel Mobility Schedule (KMS), SAT-based, SMT-based	(Tirelli et al., 2 Dec 2025, Tirelli et al., 2 Dec 2025)
Multi-Task Support	GLB/Array slices, flexible regions, DPR	(Kong et al., 2023)
Approximate Arithmetic	DRUM-k multipliers, channel-wise mapping, voltage islands	(Alexandris et al., 29 May 2025)
Multi-agent Co-design	LLM-based, iterative PPA optimization	(Jiang et al., 16 Sep 2025)
Toolchain Performance/Evaluation	II/pruning, mapping benchmarks	(Walter et al., 26 Feb 2025)
Communication/Memory Optimization	Runahead, cache reconfig., motif-based routing	(Liu et al., 13 Aug 2025, Li et al., 11 Dec 2024)

CGRAs present an efficient, reconfigurable substrate for compute-intensive workloads, with rapidly evolving methodologies for architectural exploration, optimal software mapping, energy-aware scheduling, and multi-task partitioning. The integration of behavioral simulation, hardware-software co-design, motif-centric mapping, and advanced physical synthesis is central for next-generation accelerator designs.