# OpenCL Published articles for OpenCL. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS DevFeed: [Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-09-03T13:46:18Z Content type: article Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [Artificial Intelligence](), [cpu](), [SOC](), [GPU](), [RISC-V](), [SDK](), [Android](), [Linux]() Tags: [2025](), [4g-lte](), [amlogic](), [android](), [arm](), [boardcon](), [camera](), [compiler](), [cortex-a55](), [cortex-a78](), [cpu](), [development-board](), [development-tools](), [display](), [edge-ai](), [edge-computing](), [embedded](), [embedded-systems](), [hardware](), [linux](), [npu](), [opencl](), [risc-v](), [rs485](), [som](), [specifications](), [vulkan]() ### AI overview The article examines Boardcon's CM311Y3 system-on-module, which uses the Amlogic A311Y3 octa-core SoC with Cortex-A78 and Cortex-A55 CPUs, an 8 TOPS NPU, LPDDR5 memory, eMMC storage, and interfaces for embedded applications. It also describes the module's development board, software support, and key specifications. ### Source excerpt Boardcon CM311Y3 system-on-module (SoM) is powered by an Amlogic A311Y3 octa-core Cortex-A78/A55 SoC with an 8 TOPS NPU and targets 4K video surveillance systems, AI edge computing devices, interactive terminals, enterprise thin clients, and autonomous robots. The module comes with up to 16GB LPDDR5 and up to 256 GB eMMC flash, exposes 210 pins through castellated edges, and a development board is also provided for evaluation and early software development. We don't often see new SoMs or SBCs based on Amlogic SoCs these days, so let's have a closer look. Boardcon CM311Y3 System-on-Module CM311Y3 specifications: SoC - Amlogic A311Y3 CPU 2x ARM Cortex-A78 cores @ 2.4GHx 6x ARM Cortex-A55 cores @ 2.0GHz RISC-V core for system control processing RISC-V core for Always-on power management and Sensor Hub GPU - Arm Mali-G625 MC1 with support for OpenGL ES 3.2, Vulkan 1.4, and OpenCL 3.0 VPU Video Decoder - H.264 up to [...] The post Amlogic A311Y3 Cortex-A78/A55 Edge AI system-on-module delivers up to 8 TOPS appeared first on CNX Software - Embedded Systems News. ## Orange Pi Zero 4 - Compact Allwinner A733 SBC offers HDMI, USB-C DP, GbE, WiFI 6, PCIe FFC connector for $26.5 and up DevFeed: [Orange Pi Zero 4 - Compact Allwinner A733 SBC offers HDMI, USB-C DP, GbE, WiFI 6, PCIe FFC connector for $26.5 and up]() Original publisher: [Read original article]() Author: Jean-Luc Aufranc (CNXSoft) Published: 2026-08-31T07:13:46Z Content type: news Language: en Sources: [CNX Software - Embedded Systems News]() Topics: [Embedded Systems](), [SOC](), [Arm](), [cpu](), [wifi 6](), [GPU](), [Bluetooth](), [RISC-V](), [hdmi](), [USB](), [Ethernet](), [OpenGL]() Tags: [allwinner](), [allwinner-a-series](), [android](), [arm](), [armbian](), [bluetooth](), [cortex-a55](), [cpu](), [debian](), [embedded-systems](), [gigabit-ethernet](), [gpu](), [h-264](), [hardware](), [hdmi](), [linux](), [npu](), [opencl](), [orange-pi](), [pcie](), [single-board-computer](), [specifications](), [ubuntu](), [usb](), [vp9](), [vulkan](), [wifi-6]() ### AI overview The Orange Pi Zero 4 is a compact single-board computer based on the Allwinner A733, with up to 8GB of memory, eMMC or UFS storage support, mini HDMI, USB-C DisplayPort, Gigabit Ethernet, Wi-Fi 6, Bluetooth 5.4, camera interfaces, GPIO, and a PCIe FFC connector. The article lists its specifications, planned operating-system images, and expected Armbian support. ### Source excerpt Orange Pi Zero 4 is an affordable yet feature-rich Allwinner A733 single board computer (SBC) with up to 8GB RAM, eMMC/UFS storage footprint, a microSD card slot, and plenty of I/Os. The board packs mini HDMI and USB-C DisplayPort, Gigabit Ethernet, WiFi 6 and Bluetooth 5.4, two MIPI CSI camera connectors, a Raspberry Pi-style PCIe FFC connector, and two GPIO headers into a compact 55x50mm form factor. Orange Pi Zero 4 specifications: SoC - Allwinner A733 CPU Dual-core Arm Cortex-A76 @ up to 2.00 GHz Hexa-core Arm Cortex-A55 @ up to 1.79 GHz Single-core RISC-V E902 real-time core up to 200 MHz GPU - Imagination Technologies BXM-4-64 MC1 GPU with support for OpenGL ES 3.2, Vulkan 1.3, OpenCL 3.0 VPU 8Kp24 H.265/VP9/AVS2 decoding 4Kp30 H.265/H.264 encoding AI accelerator - 3 TOPS NPU System Memory - 1GB, 2GB, 4GB, 6GB, or 8GB LPDDR5/LPDDR4/LPDDR4x; note: 16GB is also listed in the specs, [...] The post Orange Pi Zero 4 - Compact Allwinner A733 SBC offers HDMI, USB-C DP, GbE, WiFI 6, PCIe FFC connector for $26.5 and up appeared first on CNX Software - Embedded Systems News. ## OpenCL 3.1 is Here DevFeed: [OpenCL 3.1 is Here]() Original publisher: [Read original article]() Author: Khronos Group (webservices@khronosgroup.org) Published: 2026-05-04T07:01:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [cross-platform](), [Compiler](), [Open Source](), [toolchain]() Tags: [2026](), [api](), [blog-openc](), [compilation](), [compiler](), [compilers](), [cross-platform](), [directx](), [embedded](), [enumerate](), [opencl](), [optional](), [source](), [spir](), [vulkan]() ### AI overview The Khronos OpenCL Working Group has released OpenCL 3.1, moving field-proven capabilities into the core specification. The release mandates SPIR-V kernel ingestion for conformant implementations and expands cross-platform availability through a growing implementation, compiler, runtime, and framework ecosystem. ### Source excerpt On the eve of IWOCL 2026, the Khronos® OpenCL Working Group has released OpenCL™ 3.1, bringing widely deployed, field-proven capabilities into the core specification to expand functionality, including SPIR-V ingestion, that developers will be able to rely on across conformant implementations. The new specification arrives into a growing OpenCL ecosystem, with implementations from multiple silicon vendors, particularly in mobile and embedded markets, and higher-level frameworks including SYCL™ and chipStar increasingly targeting OpenCL as an acceleration backend. The open-source compiler and runtime ecosystem around OpenCL also continues to mature with layered implementations of OpenCL over Vulkan and DirectX 12 -- widening OpenCL's cross-platform availability, including on platforms without native drivers. ## OpenCL Cooperative Matrix Extensions Are Here DevFeed: [OpenCL Cooperative Matrix Extensions Are Here]() Original publisher: [Read original article]() Author: jphilips (jeff@khronosgroup.org) Published: 2026-04-29T13:00:00Z Content type: release Language: en Sources: [Blogs Khronos Blog]() Topics: [AI Inference](), [GPU](), [C](), [LLVM](), [GitHub]() Tags: [api](), [arm](), [blog-opencl-spirv-machinelearning-llv](), [c](), [gpu](), [intel](), [llvm](), [opencl](), [qualcomm](), [spir](), [vulkan]() ### AI overview The OpenCL Working Group has published a working draft of the cl_khr_cooperative_matrix extension, developed with Arm, Intel, and Qualcomm. The extension brings cooperative matrix operations for ML inference to OpenCL, while a companion OpenCL C extension is also being developed. Community feedback is requested before the standards are finalized. ### Source excerpt The OpenCL Working Group has published a draft extension (cl_khr_cooperative_matrix) that brings cooperative matrix operations--a key technology for accelerating ML inference--to OpenCL, developed in collaboration with Arm, Intel, and Qualcomm. A companion extension to expose these capabilities directly in the OpenCL C language is also in progress, and the community is invited to review both drafts and provide feedback before they are finalized. ## Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026 DevFeed: [Plan for Clojure AI, ML, and high-performance Uncomplicate ecosystem in 2026]() Original publisher: [Read original article]() Published: 2025-11-29T00:41:00Z Content type: opinion Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [Artificial Intelligence](), [Machine learning](), [CUDA](), [OpenCL](), [Programming](), [NumPy]() Tags: [2026](), [ai](), [ai-ml](), [algebra](), [apple](), [clojure](), [cuda](), [linear](), [matrices](), [neanderthal](), [opencl](), [programming](), [vectors]() ### AI overview The article outlines a 2026 development and funding plan for the Uncomplicate ecosystem of Clojure libraries for AI, machine learning, and high-performance computing. It describes support for Nvidia GPUs, Apple Silicon, CPUs, CUDA, OpenCL, and several planned library improvements. ### Source excerpt I've applied for Clojurists Together yearly funding in 2026. Here's my application. If you are a Clojurists Together member, and would like to see continued development in this area, your vote can help me keep working on this :) My goal with this funding in 2026 is to continuously develop Clojure AI, ML, and high-performance ecosystem of Uncomplicate libraries (Neanderhal and many more), on Nvidia GPUs, Apple Silicon, and traditional PC. In this year, I will also focus on writing tutorals on my blog and creating websites for the projects involved, which is something that I wanted for years, but didn't have time to do because I spent all time on programming. How that work will benefit the Clojure community This will highly benefit the Clojure community as this is THE AI ecosystem for Clojure, and supporting AI is arguably the main focus on probably all software platforms. Clojure has something to offer on that front, beyond just calling OpenAI API as a web service! Uncomplicate grew to quite a few libraries (of which some are quite big; just Neanderthal is 28,000 lines of highly-condensed, aggresively macroized, and reusable code): Diamond ONNX Runtime, Neanderthal, Deep Diamond, ClojureCUDA, ClojureCPP, Apple Presets, ClojureCL, Fluokitten, Bayadera, Clojure Sound, and Commons. Here's a word or two of how I hope to improve each of these libraries with Clojurists Together funding in 2026. Neanderthal (Clojure's alternative to NumPy, on steroids) In 2025, Neanderthal celebrated its 10th birthday. It started as a humble but fast matrix and vector library for Clojure, but after 10 years of relentless improvements, now it boasts a general matrix/vector/linear algebra API implemented by no less than 5(!) engines for CPUs, GPU (Nvidia CUDA), GPU (OpenCL: AMD, Intel, Nvidia), Apple Silicon (Accelerate), and general CPU (OpenBLAS). And this is not a superficial support for the sake of ticking a check box; each of these engines support much more operations on exotic structure ## Get Ready for Clojure, GPU, and AI in 2026 with CUDA 13.0 DevFeed: [Get Ready for Clojure, GPU, and AI in 2026 with CUDA 13.0]() Original publisher: [Read original article]() Published: 2025-10-30T16:37:00Z Content type: tutorial Language: en Sources: [Dragan Djuric]() Topics: [Clojure](), [CUDA](), [GPU](), [OpenCL]() Tags: [ai](), [c-plus-plus](), [clojure](), [coding](), [cuda](), [deep](), [diamond](), [gpu](), [opencl](), [tensors]() ### AI overview The article introduces ClojureCUDA 0.25.0, which supports CUDA 13.0.2, and encourages Clojure developers to try GPU programming interactively through the Clojure REPL. It explains that GPU acceleration is most useful for large vectors and sufficiently complex workloads because data transfer costs can outweigh computation gains. ### Source excerpt A little anniversary Did you know that CUDA has been available in Clojure for the last 9 years through ClojureCUDA, and GPU programming through OpenCL for more than 10? I almost forgot about these anniversaries. Ten years ago most people liked it a lot, starred it on Github, patted me on the back, but then concluded that they don't have an Nvidia card available on their laptops, or, if they had GPUs, that they won't have time to learn to think in massive parallel algorithms, or if they have time and will, that there are no GPUs in the servers, so what would they do with their applications, even if they created them in Clojure, and so on, and so off :) But, ClojureCUDA and ClojureCL continued living on for these 10 years, I used them in creating Neanderthal, Deep Diamond, and Diamond ML, and they proved themselves as simple and reliable tools. I still had trouble convincing Clojure programmers that they can write GPU programs that run as fast as they'd wrote them in C++, but interactively in the Cloujre REPL, without C++ hell. But I'm not easy to shake off! If it's necessary, I'll continue for 10 more years, for I'm convinced there'd be a moment when Clojure programmers are going to say "hmmm, this is something that we can use and be good at!". CUDA 13 is here! I've recently released ClojureCUDA 0.25.0, with support for the latest CUDA 13.0.2! Why not celebrate that by opening the REPL, and coding your first Hello World application on the GPU? I promise, it won't be a usual GPU carpet of text; this is ClojureCUDA, it follows the Clojure philosophy by being simple and interactive! There's not much sense in wielding a GPU to print out "Hello World". Note that it is also not very useful to work with scalar numbers and call a GPU function to add or multiply two numbers. No. Unless you have many, many, numbers to crunch, stay by your trusty CPU. For our purposes, many, many, numbers would be two vectors of dimension 3 (hey, it's hello world; imagine it's 3 billion). Also, ## Reliable AI models, simulations, and more with Gremlin's GPU experiment DevFeed: [Reliable AI models, simulations, and more with Gremlin's GPU experiment]() Original publisher: [Read original article]() Author: Andre Newman Published: 2024-12-02T00:00:00Z Content type: article Language: en Sources: [Gremlin Blog]() Topics: [GPU](), [AI Models](), [observability](), [Kubernetes]() Tags: [ai-models](), [experiment](), [features](), [gpu](), [kubernetes](), [observability](), [opencl](), [testing]() ### AI overview Gremlin's GPU experiment, called GPU Gremlin, stress-tests GPU computing capacity to help evaluate the resilience of AI models, simulations, and other GPU-intensive workloads. ### Source excerpt Build more resilient machine learning and AI models, video streaming, simulations, and more with Gremlin's GPU experiment.