RISC-V Acceleration for Deep Learning at the Edge

Artificial intelligence is no longer confined to academic theory or tech demos; it’s now driving innovation across nearly every sector, from healthcare to finance to autonomous systems. But as AI models grow in complexity and capability, the gap between their computational demands and the hardware available to run them becomes more pronounced.

What hardware limitations are slowing AI down? Why do even powerful GPUs struggle to keep up? And could open-source architectures like RISC-V hold the key to making AI deployment more efficient, especially at the edge?

The Challenges of Modern AI

Artificial intelligence has advanced rapidly, moving from experimental research to one of the most consequential technologies in the world. Its capabilities are expanding at a pace few other fields can match. Yet this growth has not been seamless; integrating AI into practical systems exposes significant challenges, many of which trace back to the hardware it relies on.

Traditional CPUs excel as general-purpose processors, capable of handling a wide variety of tasks with predictable efficiency. However, when faced with the massive parallelism and matrix operations typical of modern AI workloads, CPUs reveal their limitations. Performance often falls short, and scaling computations to meet the demands of deep learning becomes inefficient.

GPUs were developed to address some of these shortcomings. Their architecture is naturally suited to the parallel operations at the heart of neural networks, making them the workhorse of modern AI training and inference. Yet even GPUs face a problem: the pace of AI development is so fast that hardware becomes outdated quickly. A GPU that is cutting-edge today may struggle with the models of next year, forcing continual investment and upgrade cycles.

The issue is not confined to specialised accelerators. Even general-purpose devices, including CPUs, were designed with past workloads in mind. They optimise for sequential tasks and legacy software patterns, leaving modern AI tasks as awkward guests in a house built for someone else. This mismatch between hardware design and AI requirements is one of the key bottlenecks slowing deployment, increasing costs, and complicating scaling. In short, the technology exists, but the infrastructure to run it efficiently is perpetually chasing a moving target.

Bare-Metal Acceleration for Edge AI

The push to run deep learning models on edge devices clearly exposes hardware limitations that conventional CPUs and even GPUs struggle to address efficiently. Recently, researchers at University College Dublin tackled this issue by developing a 32-bit, 4-stage RISC-V System-on-Chip (SoC) that tightly integrates the open-source NVIDIA Deep Learning Accelerator (NVDLA) with a streamlined RISC-V core. The key innovation here is a bare-metal execution model: neural network models are compiled directly into RISC-V assembly code and mapped to the accelerator, eliminating the overhead of a full operating system. The result is faster execution, reduced storage requirements, and a simplified software stack, which is exactly what edge devices with limited resources need.

An important detail from the University College Dublin research is how their workflow bypasses conventional operating systems entirely. Instead of relying on middleware or drivers, neural networks are compiled straight into RISC-V instructions, tightly matched to the accelerator hardware. This not only reduces latency but also provides deterministic behaviour, a factor increasingly valuable in safety-critical applications such as industrial robotics and medical devices.

Deterministic Performance in Edge AI Systems

The SoC was implemented on a Xilinx ZCU102 FPGA and validated with models ranging from LeNet-5 to ResNet-50. Furthermore, with regards to performance, LeNet-5 inference executes in under 5 milliseconds, ResNet-18 in 16 milliseconds, and even ResNet-50 completes in roughly one second at just 100 MHz. These results highlight the efficiency gains of combining a lightweight RISC-V core with a dedicated accelerator and bypassing kernel overheads, providing a practical path for deploying AI in real-world edge scenarios.

The findings also underline a power-efficiency angle. By eliminating kernel overheads and streamlining data transfer between the CPU core and accelerator, the design achieves higher throughput per watt compared with conventional embedded GPU approaches. For edge deployments where battery operation and heat dissipation are major constraints, this balance between energy consumption and inference speed is a decisive advantage.

Efficiency and Automated Deployment Workflow

Beyond raw speed, the approach introduces an automated workflow for converting trained Caffe-based networks into configuration files and assembly code for direct hardware execution. This removes the traditional dependency on OS drivers, reduces latency, and ensures precise control over the accelerator hardware. For edge computing, where every millisecond and milliwatt counts, this architecture demonstrates a realistic, deployable solution that bridges the gap between AI capability and hardware limitations.

Another notable aspect of the implementation is its use of the open-source NVIDIA Deep Learning Accelerator (NVDLA). Leveraging open-source hardware in tandem with the modularity of RISC-V strengthens the case for transparent, verifiable AI hardware pipelines. This openness aligns with growing calls for trustworthy AI systems, where visibility into both the software and hardware stack supports reproducibility and long-term maintainability.

Could RISC-V Be the Answer to Future AI CPUs?

RISC-V stands out among processor architectures primarily because of its open-source nature and modern design. Unlike legacy ISAs, which are often constrained by decades of backward compatibility, RISC-V can be adapted, extended, and optimised for specific tasks without licensing restrictions. This flexibility makes it attractive not just for general-purpose computing, but for emerging workloads such as AI.

Recent research from researchers at University College Dublin clearly demonstrates that RISC-V can do much more than just use conventional software. By tightly coupling a RISC-V core with a deep learning accelerator and executing bare-metal code, researchers have shown that edge devices can achieve substantial gains in both speed and efficiency. This indicates that RISC-V is capable of serving as a foundational building block for AI-focused hardware, rather than merely supporting traditional computation.

Looking forward, there is potential for RISC-V designs to embrace highly parallel architectures. Multi-core RISC-V CPUs could combine general-purpose processing with AI acceleration, leveraging parallelism without the overhead typical of GPUs. Such designs would offer a middle ground: the flexibility and software compatibility of standard CPUs, with performance enhancements approaching those of specialised accelerators. In essence, RISC-V’s modularity and openness may provide a practical path toward CPUs designed specifically with AI workloads in mind.