{"slug": "microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson", "title": "MicroPhase Ships AntSDR T510 AI Combining RFSoC and Jetson", "summary": "MicroPhase Technology announced the AntSDR T510 AI, a single-board platform combining an AMD Zynq UltraScale+ RFSoC with an NVIDIA Jetson module for RF capture and GPU-accelerated AI inference. The board, listed as coming soon on Crowd Supply, offers eight 14-bit ADC and DAC channels, 8T8R synchronized operation, and RF coverage from 1 MHz to 6 GHz. This integration reduces latency and simplifies timing in phased-array, massive MIMO, and spectrum-sensing research.", "body_md": "# MicroPhase Ships AntSDR T510 AI Combining RFSoC and Jetson\n\nMicroPhase Technology has announced the AntSDR T510 AI, a single-board platform listed as \"coming soon\" on Crowd Supply that pairs an AMD Zynq UltraScale+ RFSoC ZU47DR with an NVIDIA Jetson module to perform RF capture, deterministic RF-domain processing, and GPU-accelerated AI inference on one board. Per the Crowd Supply listing, the board offers eight 14-bit ADC channels sampling up to 5 GSPS, eight 14-bit DAC channels up to 9.85 GSPS, an 8T8R synchronized transmit/receive architecture, and RF coverage from 1 MHz to 6 GHz. Platforms that colocate RF front ends and GPU acceleration reduce interconnect latency and simplify timing chains in phased-array, massive MIMO, and spectrum-sensing research, shortening development cycles for edge wireless systems.\n\n### What happened\n\nMicroPhase Technology announced the AntSDR T510 AI, a crowdfunding campaign listed as \"launching soon\" on Crowd Supply. The platform integrates an AMD Zynq UltraScale+ RFSoC ZU47DR alongside an NVIDIA Jetson module on a single board. Per the Crowd Supply listing, the board provides eight 14-bit ADC channels sampling to 5 GSPS, eight 14-bit DAC channels to 9.85 GSPS, synchronized 8T8R operation, and direct RF coverage from 1 MHz to 6 GHz. Hackster covered the announcement, describing the board as capable of capturing, processing, analyzing, and responding to RF signals without external systems.\n\n### Technical details\n\nPer the Crowd Supply page, the RFSoC handles deterministic RF tasks such as digital upconversion/downconversion, interpolation, decimation, and multi-channel synchronization, while the Jetson module (listed as Jetson NX at 15W typical power) supplies GPU-accelerated AI workloads. Each RF channel supports up to 2 GHz of baseband bandwidth. The design includes a 100G QSFP28 optical output, multi-board synchronization for scaling to 16, 32, or more channels, and typical power consumption of 45W under full 8-channel operation plus Jetson NX. Storage includes 4GB + 2GB DDR4, 32GB eMMC, and M.2 SSD expansion.\n\n### Software and open-source\n\nThe platform ships Ubuntu 22.04 with CUDA pre-configured. Bundled tools include WaveSight (multi-channel RF visualization and replay), GNU Radio and SoapySDR compatibility, and the open-source IQTAXI driver framework for RFSoC control and IQ data acquisition. A \"SignalLab AI\" demo provides GPU-accelerated real-time classification of Wi-Fi, Bluetooth, and modulation types from captured RF data. MicroPhase plans to publish hardware reference files, RFSoC firmware source, and Jetson integration examples in the public MicroPhase/T510-AI GitHub repository.\n\n### Industry context\n\nColocating high-speed RF data converters and local GPU acceleration is an emerging pattern for edge wireless research, because it reduces data-movement overhead and cross-device synchronization complexity in applications like phased-array radar, massive MIMO, and intelligent spectrum monitoring. Developers working on real-time ML-in-the-loop signal processing often face latency and bandwidth limits when splitting acquisition and inference across separate systems.\n\n### What to watch\n\nPricing and campaign launch date have not yet been announced on Crowd Supply. Practitioners should monitor SDK maturity - specifically the data path from RFSoC fabric into Jetson GPU - plus power and thermal performance under sustained wideband workloads. The 100G QSFP28 interface and multi-board synchronization path are notable for large-array prototyping.\n\n## Scoring Rationale\n\nInteresting specialized hardware integrating RFSoC and GPU on one board, with a solid open-source software stack, but the campaign has not yet launched and the audience is narrow (RF/edge wireless research). Relevant to practitioners in phased-array, massive MIMO, and spectrum-sensing domains, but too niche and early-stage to rank above Solid.\n\nPractice with real Telecom & ISP data\n\n90 SQL & Python problems · 15 industry datasets\n\n[Active Residential CustomersEasy](/problems/sql/active-residential-customers)\n\n[Unlimited Fiber Plans 500Mbps+Medium](/problems/sql/unlimited-fiber-plans-above-500mbps)\n\n[Customer Churn Risk AssessmentHard](/problems/sql/customer-churn-risk-assessment)\n\n250 free problems · No credit card\n\n[See all Telecom & ISP problems](/problems/datasets/telecom)", "url": "https://wpnews.pro/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson", "canonical_source": "https://letsdatascience.com/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson-41fa8b88", "published_at": "2026-06-20 22:08:36.202003+00:00", "updated_at": "2026-06-20 22:08:38.879506+00:00", "lang": "en", "topics": ["ai-chips", "ai-infrastructure", "ai-tools", "ai-research", "ai-products"], "entities": ["MicroPhase Technology", "AMD", "NVIDIA", "Crowd Supply", "AntSDR T510 AI", "Zynq UltraScale+ RFSoC ZU47DR", "Jetson", "Hackster"], "alternates": {"html": "https://wpnews.pro/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson", "markdown": "https://wpnews.pro/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson.md", "text": "https://wpnews.pro/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson.txt", "jsonld": "https://wpnews.pro/news/microphase-ships-antsdr-t510-ai-combining-rfsoc-and-jetson.jsonld"}}