Introducing the Altera Community AI Responder — Beta
Altera is introducing the AI Responder, an assistant built into the Altera Community to help provide faster, context-aware guidance for Altera product questions. It uses relevant public Altera documentation and Knowledge Database content, with coverage continuing to expand. The AI Responder may provide source references and can hand off complex or out-of-scope questions to an Altera subject-matter expert. As a beta feature, responses may contain errors or omissions, so please review the sources and verify technical guidance against official Altera documentation before implementation. If the response does not fully address your question, add more context and tag ai_responder to continue the discussion. We welcome your feedback as we improve the Community experience.How does the FPGA AI Suite utilize Agilex 5 DSP Blocks?
Hello Altera Community. I have a module called "dot_bf16" that uses the bf16 floating point format to calculate dot product and I am wondering if it uses the Agilex5 AI optimized DSP blocks to do this. I can't myself see how the DSP blocks are configured because they are all encrypted. 1. Agilex™ 5 Variable Precision DSP Blocks Overview • Variable Precision DSP Blocks User Guide Agilex™ 5 FPGAs and SoCs • Altera Documentation and Resources Center This document states that there are 3 operational modes for tensor mode: 1. Tensor float point mode 2. Tensor fixed point mode 3. Tensor accumulation mode Does the FPGA AI Suite utilize any of these? I also noticed the module uses a FSM and controller.
Is Spatial IP ready for LLM / transformer inference?
I am using FPGA AI Suite 2026.1.1 (with the new spatial compiler). Most of the FPGA AI Suite handbook examples I see are classical CNN / vision flows (ResNet-style) on PCIe, hostless JTAG, and SoC. Is transformer / LLM inference (attention layers, variable sequence lengths, large KV-cache activations, etc.) something we can target today with dla_compiler + Spatial IP, or is Spatial still aimed primarily at CNN-like graphs, or is custom RTL expected? And if yes, are there any LLM examples, guides, recommended flows, or known limitations? Thanks,How can I port RTL module into oneAPI FPGA programming ?
Hi there, I have been using oneAPI for FPGA programming for a while. I am now trying to port my well-defined RTL module into my oneAPI implementation. I do read and understand the specification documents provided in oneAPI websites also the openCL SDK development. However, one of these have pointed a very clear way to interact with RTL module using oneAPI. I am successfully runing a combinational vector-add sample myself, but have no idea how to interact with the sythesized modules from oneAPI with clock driven capability and Avalon-ST interface. Have anyone done such a tryout before ? Best.
