Accelerating HDC-CNN Hybrid Models Using Custom Instructions on RISC-V GPUs

• URL: http://arxiv.org/abs/2511.05053v1
• Date: Fri, 07 Nov 2025 07:50:48 GMT
• Title: Accelerating HDC-CNN Hybrid Models Using Custom Instructions on RISC-V GPUs
• Abstract summary: Machine learning based on neural networks has advanced rapidly, but the high energy consumption required for training and inference remains a major challenge.<n>To overcome this, hybrid accelerators combining HDC and CNNs have been proposed, though their adoption is limited by poor generalizability and programmability.<n>In this study, we design and implement custom GPU instructions optimized for HDC operations, enabling efficient processing for hybrid HDC-CNN workloads.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Machine learning based on neural networks has advanced rapidly, but the high energy consumption required for training and inference remains a major challenge. Hyperdimensional Computing (HDC) offers a lightweight, brain-inspired alternative that enables high parallelism but often suffers from lower accuracy on complex visual tasks. To overcome this, hybrid accelerators combining HDC and Convolutional Neural Networks (CNNs) have been proposed, though their adoption is limited by poor generalizability and programmability. The rise of open-source RISC-V architectures has created new opportunities for domain-specific GPU design. Unlike traditional proprietary GPUs, emerging RISC-V-based GPUs provide flexible, programmable platforms suitable for custom computation models such as HDC. In this study, we design and implement custom GPU instructions optimized for HDC operations, enabling efficient processing for hybrid HDC-CNN workloads. Experimental results using four types of custom HDC instructions show a performance improvement of up to 56.2 times in microbenchmark tests, demonstrating the potential of RISC-V GPUs for energy-efficient, high-performance computing.

Related papers

• Compiler-Driven Approximation Tuning for Hyperdimensional Computing [8.069566071125822]
  Hyperdimensional Computing (HDC) represents an alternative to conventional deep learning techniques.<n>In this work, we introduce ApproxHDC, a framework for automated identification and application of domain-specific approximations in HDC workloads.
  arXiv  Detail & Related papers  (2026-06-25T02:46:49Z)
• Convex Optimization for Alignment and Preference Learning on a Single GPU [52.997197698288936]
  Fine-tuning large language models to align with human preferences has driven the success of systems such as Gemini and ChatGPT.<n>Direct Preference Optimization (DPO) offers a simpler alternative but has limitations such as inconsistent ranking accuracy and high dependence on GPU resources.<n>We propose the Convex Optimization for Alignment and Preference Learning Algorithm (COALA), a novel lightweight strategy with strong theoretical guarantees.
  arXiv  Detail & Related papers  (2026-05-22T05:25:00Z)
• ImageHD: Energy-Efficient On-Device Continual Learning of Visual Representations via Hyperdimensional Computing [0.07646713951724012]
  ImageHD is an FPGA accelerator for on-device continual learning of visual data based on HDC.<n>On CORe50, ImageHD achieves up to 40.4x (4.84x) speedup and 383x (105.1x) energy efficiency over optimized CPU ( GPU) baselines.
  arXiv  Detail & Related papers  (2026-04-23T04:55:18Z)
• CUDA Agent: Large-Scale Agentic RL for High-Performance CUDA Kernel Generation [51.72529978689561]
  Agent is a large-scale agentic reinforcement learning system that develops kernel expertise through three components.<n>Agent delivers 100%, 100%, and 92% faster rate over torchcompile on KernelBench.
  arXiv  Detail & Related papers  (2026-02-27T18:58:05Z)
• GaDE -- GPU-acceleration of time-dependent Dirac Equation for exascale [0.0]
  GaDE is designed to simulate the electron dynamics in atoms induced by electromagnetic fields in the relativistic regime.<n>We evaluate GaDE on the pre-exascale supercomputer LUMI, powered by AMD MI250X GPUs and Hewlett-Packard's Slingshot interconnect.
  arXiv  Detail & Related papers  (2025-12-25T14:47:36Z)
• Distributed Equivariant Graph Neural Networks for Large-Scale Electronic Structure Prediction [76.62155593340763]
  Equivariant Graph Neural Networks (eGNNs) trained on density-functional theory (DFT) data can potentially perform electronic structure prediction at unprecedented scales.<n>However, the graph representations required for this task tend to be densely connected.<n>We present a distributed eGNN implementation which leverages direct GPU communication and introduce a partitioning strategy of the input graph.
  arXiv  Detail & Related papers  (2025-07-04T23:53:47Z)
• QuartDepth: Post-Training Quantization for Real-Time Depth Estimation on the Edge [55.75103034526652]
  We propose QuartDepth which adopts post-training quantization to quantize MDE models with hardware accelerations for ASICs.<n>Our approach involves quantizing both weights and activations to 4-bit precision, reducing the model size and computation cost.<n>We design a flexible and programmable hardware accelerator by supporting kernel fusion and customized instruction programmability.
  arXiv  Detail & Related papers  (2025-03-20T21:03:10Z)
• Multi-GPU RI-HF Energies and Analytic Gradients $-$ Towards High Throughput Ab Initio Molecular Dynamics [0.0]
  This article presents an optimized algorithm and implementation for calculating resolution-of-the-identity Hartree-Fock energies and analytic gradients using multiple Graphics Processing Units (GPUs) The algorithm is especially designed for high throughput emphab initio molecular dynamics simulations of small and medium size molecules (10-100 atoms)
  arXiv  Detail & Related papers  (2024-07-29T00:14:10Z)
• HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning [18.790512589967875]
  Brain-inspired HyperDimensional Computing (HDC) has been introduced as a promising solution for lightweight machine learning. In this paper, we leverage HDC for an intrinsically more efficient and acceleration-friendly Knowledge Graph Completion (KGC) algorithm. We also co-design an acceleration framework named HDReason targeting FPGA platforms.
  arXiv  Detail & Related papers  (2024-03-09T02:17:43Z)
• Towards Efficient Hyperdimensional Computing Using Photonics [15.975464191800773]
  Hyperdimensional Computing (HDC) is an emerging, brain-inspired machine learning technique. In this paper, we argue that photonic computing and HDC complement each other better than photonic computing and Deep Neural Networks (DNNs) We propose PhotoHDC, the first-ever electro-photonic accelerator for HDC training and inference.
  arXiv  Detail & Related papers  (2023-11-29T16:51:21Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• Reconfigurable Distributed FPGA Cluster Design for Deep Learning Accelerators [59.11160990637615]
  We propose a distributed system based on lowpower embedded FPGAs designed for edge computing applications. The proposed system can simultaneously execute diverse Neural Network (NN) models, arrange the graph in a pipeline structure, and manually allocate greater resources to the most computationally intensive layers of the NN graph.
  arXiv  Detail & Related papers  (2023-05-24T16:08:55Z)
• HDTorch: Accelerating Hyperdimensional Computing with GP-GPUs for Design Space Exploration [4.783565770657063]
  We introduce HDTorch, an open-source, PyTorch-based HDC library with extensions for hypervector operations. We analyze four HDC benchmark datasets in terms of accuracy, runtime, and memory consumption. We perform the first-ever HD training and inference analysis of the entirety of the CHB-MIT EEG epilepsy database.
  arXiv  Detail & Related papers  (2022-06-09T19:46:08Z)
• FPGA-optimized Hardware acceleration for Spiking Neural Networks [69.49429223251178]
  This work presents the development of a hardware accelerator for an SNN, with off-line training, applied to an image recognition task. The design targets a Xilinx Artix-7 FPGA, using in total around the 40% of the available hardware resources. It reduces the classification time by three orders of magnitude, with a small 4.5% impact on the accuracy, if compared to its software, full precision counterpart.
  arXiv  Detail & Related papers  (2022-01-18T13:59:22Z)
• The Architectural Implications of Distributed Reinforcement Learning on CPU-GPU Systems [45.479582612113205]
  We show how to improve the performance and power efficiency of RL training on CPU-GPU systems. We quantify the overall hardware utilization on a state-of-the-art distributed RL training framework. We also introduce a new system design metric, CPU/GPU ratio, and show how to find the optimal balance between CPU and GPU resources.
  arXiv  Detail & Related papers  (2020-12-08T04:50:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.