Bendable RISC-V Core Architecture

• Bendable RISC-V cores are highly configurable processor architectures that use modular, parameterized designs to allow rapid prototyping and tailored performance.
• They integrate custom instruction extensions, dynamic micro-decoding, and resource allocation techniques to optimize for power, area, and throughput based on application needs.
• Real-world applications include FPGA prototyping, automotive embedded systems, and scalable multi-core deployments that balance energy efficiency with high-performance computing.

A Bendable RISC-V core is an adaptive processor architecture conforming to the open RISC-V ISA, engineered for configurability, extensibility, and operational flexibility across a wide range of application and deployment scenarios. This term encompasses a set of microarchitectural, system integration, and design-space exploration mechanisms—spanning resource allocation, instruction set extensions, thread management, reconfigurable datapaths, and accelerator interfacing—that collectively enable the underlying RISC-V core to be “bent” or tailored to diverse performance, power, area, and workload constraints.

1. Architectural Modularity and Parameterization

Bendable RISC-V cores are constructed to maximize modularity at both core and system levels. At the microarchitectural level, this includes:

• Granular modular decomposition (pipeline stages, arithmetic units, memory interfaces) with well-defined interfaces, facilitating independent modification or substitution of components (Bandara et al., 2019).
• Highly parameterized RTL (register-transfer level) descriptions, whereby usable parameters such as pipeline depth, cache geometry (e.g., number of sets/ways/line size), interconnect topologies, or even supported instruction and device extensions are exposed to the designer. For instance, in BRISC-V, cache size is controlled by $$2^{\text{INDEX\_BITS}} \cdot 2^{\text{OFFSET\_BITS}} \cdot \text{NUMBER\_OF\_WAYS}$$, enabling rapid exploration of cache organizations (Bandara et al., 2019).
• “Plug-and-play” bus standard adherence (AXI, AHB, TileLink, etc.), which allows seamless integration of the core into different SoC fabrics, MPSoCs, or network-on-chip (NoC) environments (Silva et al., 25 Jun 2024).

This architectural “bendability” underpins rapid prototyping, RTL simulation/emulation, and incremental hardware modification, significantly reducing the latency of design space exploration and hardware/software codesign (Bandara et al., 2019, Merchant et al., 2021, Silva et al., 25 Jun 2024).

2. Custom Extensions and Instruction-Set Flexibility

Bendable RISC-V cores typically leverage the ISA’s provision for custom extensions and modular instruction adoption:

• Integration of domain-specific operations as custom instructions, directly in the execution pipeline, as exemplified by cryptographic primitives (e.g., hardware-supported Montgomery multiplication in modular arithmetic for public key cryptography) (Irmak et al., 2020), SIMD vectorization for DSP workloads (Gautschi et al., 2016), or dynamic macro-instruction support via an on-chip micro-decoder (Pottier et al., 21 Jun 2024).
• Selective inclusion or exclusion of standard extensions (e.g., M for multiplication/division, A for atomics, C for compressed instructions), thereby optimizing area and power for the intended application. For example, the use of the compressed instruction extension (C) and embedded (E) reduces code size and area in ultra-low-power designs (Irmak et al., 2020, Silva et al., 25 Jun 2024).
• Dynamic micro-decoding units can translate CISC-like macro-instructions into microcoded RISC-V instruction sequences at runtime—a bendable approach allowing binary compression, security obfuscation, and even post-silicon architectural updates (Pottier et al., 21 Jun 2024).

These mechanisms ensure that the single core design is not rigidly tied to a static feature set but may be extended or trimmed to efficiently fit the functional envelope required by the deployment context.

3. Scalability through Multi-Core, Clustering, and Thread Management

Many bendable RISC-V cores address scalability via:

• Modular cluster-based scalable tile architectures, where cores are organized in shared memory or message-passing clusters (e.g., the GRVI Phalanx system with 8-core clusters, each sharing IRAM and CRAM memories, interconnected via a 300-bit Hoplite NoC) (Gray, 2016).
• Support for different concurrency models—single-issue, pipeline-interleaved multi-threading, and concurrent multi-core clusters. For example, Klessydra cores implement interleaved multi-threading with a hardware thread counter and per-thread register files, supporting real-time concurrency scalability for IoT workloads (Cheikh et al., 2017).
• Dynamic management of computational resources through DVFS (Dynamic Voltage and Frequency Scaling), near-threshold operation (enabling range from kOPS to GOPS in IoT endpoints) (Gautschi et al., 2016), and runtime reconfiguration (Spatzformer merge/split vector-scalar cluster modes for mixed scalar-vector workloads) (Perotti et al., 7 Jul 2024).

These features provide fine-grained control over parallelism, throughput, and energy/power trade-off, directly contributing to the “bendability” across performance and efficiency axes.

4. Heterogeneous Integration and Accelerator Coupling

Bendable RISC-V cores are often deployed in heterogeneous system environments:

• Integration with accelerators via shared memory buses, custom coprocessor ports, or memory-mapped interfaces, leveraging parameterized and standardized interconnects (e.g., the Phalanx system’s accelerator-attachable clusters and the ESP platform’s accelerator-centric NoC tiles) (Gray, 2016, Zuckerman et al., 2022).
• Flexible coupling to external ML, DSP, or cryptographic units: ML co-processors for SVM inference in flexible (bit-serial) electronics (Vergos et al., 27 Aug 2025); approximate arithmetic units in reconfigurable, fault-tolerant cores for energy-critical applications (Delavari et al., 1 Oct 2024); posit arithmetic units (PAU) for fused quire operations alongside IEEE 754 floats in high-precision compute environments (Mallasén et al., 2021).
• Accommodation of heterogeneous-ISA operation (e.g., RISC-V alongside SPARC in JuxtaPiton, with shared cache, coherence, and interrupt fabric—facilitated by “transducers” between memory buses) (Lim et al., 2018).

This enables system-level “bendability”: designs can not only alter or extend core behavior but can also mix, match, or dynamically allocate accelerator, core, and peripheral resources as needed.

5. Power, Area, and Energy Efficiency Trade-offs

Bendable RISC-V core designs deliberately expose or automate the optimization of power, energy, and resource utilization:

• Minimal resource footprints enabled by microarchitectural minimization (e.g., SERV bit-serial datapath (Vergos et al., 27 Aug 2025)), modularity (NoX, 27kGE, 2665 LUTs (Silva et al., 25 Jun 2024)), or elimination of unneeded units (sharing shifters/multipliers within a cluster, as in Phalanx (Gray, 2016)).
• Support for multi-voltage operation, including near-threshold designs achieving 67–193 MOPS/mW in 65–28 nm CMOS (Gautschi et al., 2016) and dynamic adjustment of energy-accuracy via on-the-fly circuit selection (phoeniX platform) (Delavari et al., 1 Oct 2024).
• Measured platform-level metrics: e.g., 100,000 MIPS at 13 W for 400-core FPGA systems (Gray, 2016); energy efficiency gains of 3.2× to 10× via SIMD extensions and L0 buffering (Gautschi et al., 2016); up to 21× speedup and energy reduction in flexible ML acceleration (Vergos et al., 27 Aug 2025).

This deliberate design and measurement of resource/performance trade-offs ensures that the bendable core paradigm is suitable for diverse deployment scenarios, from embedded real-time to high-performance and machine learning inference.

6. Real-World Applications and System Prototyping

Bendable RISC-V cores are evidenced in practice through:

• Rapid deployment and validation via FPGA prototyping and web-based configuration (e.g., BRISC-V with browser-based RTL and system simulation (Bandara et al., 2019), portable Linux-capable RISC-V platforms for low-cost FPGA boards (Miura et al., 2020)).
• Prototyped MPSoCs (ANDROMEDA, Synopsys HAPS-80D Dual platform (Merchant et al., 2021); NoX, FreeRTOS support (Silva et al., 25 Jun 2024)) capable of benchmarking with STREAM, matrix multiply, and N-body simulations.
• Real-world workloads including image processing (phoeniX, PSNR-vs-energy tuning (Delavari et al., 1 Oct 2024)), SVM ML inference on flexible substrates (SERV-plus-accelerator (Vergos et al., 27 Aug 2025)), and accelerator-centric SoCs for automotive and embedded HPC (CVA6S+ with high-bandwidth HPDCache (Tedeschi et al., 20 Apr 2025)).

The flexibility to deploy, adapt, and validate new system architectures at low cost or in emerging domains underscores the practical value of architectural bendability.

7. Limitations, Open Challenges, and Future Directions

While bendable RISC-V architectures achieve broad adaptability, certain limitations and open questions remain:

• Complexity/overhead trade-offs: Enhanced control structures (e.g., distributed control or micro-decoder pipeline slices) introduce non-trivial area and timing overheads, though typically modest (e.g., 1.4% for Spatzformer reconfigurability (Perotti et al., 7 Jul 2024), ~4% for micro-decoder pipeline (Pottier et al., 21 Jun 2024)).
• Accurate/approximate operation safety: Fine-grained approximate arithmetic (phoeniX (Delavari et al., 1 Oct 2024)) necessitates robust configuration and error monitoring to guarantee application-level correctness where required.
• State/context management in extensions: For instance, posit-quire accumulation in PAU is limited to a single accumulator, constraining multitasking and preemption (PERCIVAL (Mallasén et al., 2021)).
• The challenge of efficiently scaling to large, accelerator-rich systems while maintaining standardized, low-latency, and coherent memory/interconnect protocols (ESP, Phalanx (Gray, 2016, Zuckerman et al., 2022)).
• Toolchain and software-stack support for rapidly reconfigured or custom-extended cores, as illustrated by Xposit-LLVM integration (Mallasén et al., 2021).

Research in dynamic microcode adaptation, transparent context management, automated design-space exploration, and deeper software-hardware co-design is ongoing to address these concerns.

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In summary, bendable RISC-V cores represent a paradigm characterized by open, modular, and highly configurable processor architectures that can be adaptively tailored—via parameterization, extension, clustering, heterogeneity, and run-time reconfiguration—to a broad spectrum of application domains and performance constraints, while maintaining the core tenets of ISA openness and hardware-software co-evolution.