GAP9 RISC-V SoC for Edge & IoT

Updated 22 July 2025

GAP9 RISC-V SoC is an ultra-low-power microprocessor platform that leverages an open RISC-V ISA with multi-core and DSP extensions for edge and IoT applications.
It employs high parallelism with configurable pipelines and memory hierarchies, achieving performance improvements up to 118 MIPS while maintaining energy efficiency.
The platform features robust security measures including Trusted Hart and dynamic enclave support, ensuring reliable protection in resource-constrained environments.

The GAP9 RISC-V System-on-Chip (SoC) is an advanced, ultra-low-power microprocessor platform designed specifically to address the computational and energy constraints of edge and IoT applications. Building upon the open RISC-V instruction set architecture (ISA), GAP9 integrates a cluster of RISC-V compliant cores, DSP extensions, memory hierarchy, and heterogeneous processing units, embodying a design philosophy that emphasizes high parallelism, configurability, and efficient system-level integration. GAP9 serves as a reference implementation for IoT end-nodes requiring significant computational throughput, deterministic real-time performance, and enhanced security, while operating within stringent power budgets.

1. Microarchitecture and Core Design

At the heart of the GAP9 SoC is a multi-core RISC-V processor cluster, frequently built around the RV32I base integer instruction set and microarchitecture patterns optimized for resource-constrained environments (Cheikh et al., 2017). The core design typically divides into a modular, three-stage or four-stage pipeline (e.g., instruction fetch, decode, execute, writeback), leveraging architectural features such as:

Hardware Multi-threading: Some core implementations support interleaved multi-threading, whereby instructions from different hardware threads are fetched and executed in a round-robin manner. This approach masks memory latencies and control hazards by selecting among active hardware threads using a thread counter (“harc”), skipping inactive ones and inserting NOPs to preserve pipeline efficiency.
Control and Status Registers (CSR): Fully supports M-mode privileged instructions, certain atomic operations, and a state machine-controlled CSR block for handling system-level events, exceptions, and context switching.
Debug Support: Integration with established debug interfaces (e.g., Pulpino debug unit) allows for fine-grained control during software development and hardware validation.

Such designs are distinguished by their simplicity in hazard management––relying on thread interleaving rather than complex interlocks, thus reducing hardware area and power consumption—attributes that are critical for IoT deployment.

2. Memory Hierarchy and System Integration

The memory subsystem and integration with specialized hardware units are key aspects of GAP9 SoC architecture (Bandara et al., 2019):

Memory Organization: Typically features a combination of tightly-coupled data memory (TCDM), multi-level (e.g., L1/L2) caches, and on-chip SRAM, often with a flexible bus interface (AXI, AHBv5). Modular RTL designs allow fine-tuning of cache policies (write-back, write-allocate) and parameters (associativity, set/line size), crucial for balancing performance and energy requirements.
Heterogeneous Units: The RISC-V cluster may be surrounded by dedicated accelerators (for vector processing, neural network inference, DSP kernels), cryptographic offloads, and DMA engines, under the orchestration of the central fabric controller.
System Topology: The device is amenable to bus-based or NoC (network-on-chip) topologies, with parameterizable router architectures (buffered or bufferless, with various routing schemes), enabling scalable system designs that balance latency, bandwidth, and area overhead for diverse workloads.

3. Parallelism, Performance, and Energy Efficiency

GAP9 is designed to exploit parallelism at both the thread and core levels, supporting concurrent execution across multiple processing elements. Such parallelization is critical for real-time signal processing, vibration diagnostics, and neural network inference at the edge.

Performance Metrics: Multi-threaded, multi-core configurations yield substantial throughput improvements. For example, measured implementations have achieved up to 118.09 MIPS (million instructions per second) via increased thread pool size and optimal pipeline clocking (Cheikh et al., 2017).
Energy Efficiency: The use of deep sub-threshold operation, aggressive clock gating, and small-core architectures ensure minimum energy per operation. Real-world deployments—such as the PARSY-VDD end-to-end vibration diagnostics—demonstrate execution times as low as 751 μs at 370 MHz and 0.8 V, and energy consumption down to 37 μJ at 240 MHz and 0.65 V when leveraging the parallel compute cluster (Kiamarzi et al., 7 Apr 2025).
Application Acceleration: Algorithmic optimization and hardware feature exploitation (e.g., hardware loops, SIMD, post-increment load/store) yield orders-of-magnitude improvements in time and energy for computationally intensive tasks.

4. Software, Design Exploration, and Simulation Ecosystem

A robust development and simulation ecosystem supports GAP9 configuration, validation, and optimization:

Design Space Exploration: Platforms such as BRISC-V provide RTL-level modularity, parameterization, and graphical configuration (GUI), empowering designers to instantiate and explore distinct core, memory, and interconnect topologies rapidly (Bandara et al., 2019).
Simulation and Performance Modeling: With tools such as GVSoC, architects can conduct full-platform, event-driven simulations with high functional and timing accuracy, exploiting Python/JSON-based modular configuration. GVSoC achieves up to 2500× speed-up over cycle-accurate simulation, with less than 10% performance estimation error, enabling agile architectural tuning and analysis (Bruschi et al., 2022).
Verification Frameworks: FPGA-assisted emulation (e.g., FERIVer) offers efficient cross-verification of instruction-level and cycle-accurate execution, running at approximately 5 MIPS—over 150× faster than pure software simulation tools. Such frameworks facilitate early bug detection and low-cost debugging in complex SoC designs (Qin et al., 7 Apr 2025).

5. Security Architecture and Trusted Execution

Security in the GAP9 SoC is strengthened through the deployment of isolation and trust mechanisms:

Trusted Hart Architecture: Designs may incorporate a dedicated core (“Trusted Hart”) executing a trusted OS (e.g., seL4), responsible for persistent security functions (key management, attestation, peripheral control), thus reducing the trusted computing base for security-critical operations (Ushakov et al., 2022).
Dynamic Enclave Support: Using frameworks like Keystone, dynamic enclaves are instantiated with fine-grained memory protection (leveraging RISC-V PMP and secure monitor logic), enabling the secure execution of sensitive code and support for GlobalPlatform TEE APIs.
Root of Trust and Attestation: Secure boot mechanisms facilitate the derivation of attestation keys, with formal endorsement chains represented by signatures such as:

$\text{ENDORSE}_{\text{TH}} = \text{SHA}_{\text{TH}} \,\|\, \text{PUB}_{\text{TH}} \,\|\, \text{SGN}_{\text{SM}},$

where attestation keys are chained between secure boot stages, ensuring the integrity and authenticity of the security infrastructure.

GAP9 targets advanced edge applications, including TinyML, structural health monitoring, and autonomous sensor nodes:

On-Device Analytics: Implementations like PARSY-VDD fully exploit multicore capabilities for vibration-based damage detection via parallelized system identification and spectral estimation, achieving nearly 90× speed and 85× energy improvement over previous state-of-the-art on the same platform (Kiamarzi et al., 7 Apr 2025).
TinyML and DNN Inference: While GAP9 typically emphasizes 8-bit or sub-8-bit DNN inference, related works (e.g., DARKSIDE) extend the paradigm to include mixed-precision computation (2–32 bits), fused MAC-Load operations, and dedicated accelerators for floating-point tensor multiplication—enabling on-device floating-point training at the extreme edge (Garofalo et al., 2023).
Comparative Analysis: Compared to alternative RISC-V SoCs, GAP9 is characterized by its balanced mix of configurability, multicore parallelism, open ISA support, and efficient power/performance trade-offs, albeit with less focus on ultra-mixed-precision or floating-point training accelerators as found in more specialized designs.

7. Modularity, Scalability, and Future Directions

GAP9’s modular, RISC-V-centric architecture positions it for scalability and ongoing evolution:

Plug-and-Play Cores: Open-source and compact cores such as NoX, with a four-stage, single-issue in-order pipeline and full bypassing, can be seamlessly integrated into GAP9 SoCs to enhance heterogeneity and optimize performance per area and energy (Silva et al., 25 Jun 2024).
Incremental Enhancement: Well-defined, decoupled interfaces between system components (cores, caches, routers, accelerators) allow targeted upgrades—such as cache policy modification or branch predictor integration—without extensive system redesign (Bandara et al., 2019).
Verification and Validation: High-speed, low-resource FPGA-based frameworks enable rapid, cycle-accurate verification of evolving hardware designs, underpinning the iterative development of robust, secure, and efficient SoCs (Qin et al., 7 Apr 2025).
Application Ecosystem: The combination of robust simulation, design exploration, hardware acceleration, and security mechanisms makes GAP9 a foundational platform for edge intelligence, shaping future IoT and embedded systems.

Table: Illustrative Metrics for GAP9 and Related RISC-V SoCs

Feature	GAP9 Example	Related SoCs (e.g., DARKSIDE, NoX)
Peak Performance	~118 MIPS (Cheikh et al., 2017)	65 GOPS (2-bit DNN) (Garofalo et al., 2023); NoX: 2.5 CoreMark/MHz (Silva et al., 25 Jun 2024)
Cluster Size	Up to 9 cores	8 (DARKSIDE); 1 (NoX); configurable (BRISC-V)
Energy Consumption	37 μJ/run (Kiamarzi et al., 7 Apr 2025)	835 GOPS/W (DARKSIDE); optimized per tile (NoX)
Security Model	Trusted Hart, Enclaves	Varies; NoX: CSR/trap support
Verification Method	FPGA-accelerated, GVSoC	FERIVer (~5 MIPS)

GAP9 represents a comprehensive, modular, and energy-efficient RISC-V SoC, with demonstrated strengths in IoT, edge analytics, parallel data processing, and secure system design. Its evolution is driven by robust toolchains, simulation platforms, and architectural flexibility that supports rapid deployment and domain-specific customization across a wide range of embedded applications.