NETINT Technologies Inc.’s Post

One common misconception about ASICs is that they are inflexible and compromise on video quality. 🤨 However, this couldn't be further from the truth! 🚫 Let's set the record straight: - High-Quality Encoding: ASICs have been the backbone of MPEG-II and H.264 encoders for years, delivering exceptional video quality in broadcast environments. - Adaptability: Modern ASIC designs offer flexibility and can adapt to evolving video encoding standards and requirements. - Industry Acceptance: Leading video engineers, like David Ronca from Netflix, recognize the inevitable shift towards video processing ASICs. Have you ever wondered how YouTube manages to stay ahead of the competition? While CPU and GPU encoding have their place, they often fall short in meeting the demands of modern video processing. Platforms that rely solely on these methods face several challenges: - 📉 Reduced Quality: Limited by computational resources, platforms may have to compromise on resolution, frame rate, or other quality parameters. - 🐌 Slower Encoding: CPU and GPU encoding can be time-consuming, hindering the ability to process large volumes of video content efficiently. - 💸 Higher Costs: The infrastructure required to support CPU and GPU encoding at scale can be expensive, impacting profitability and competitiveness. Reach out to our team of experts today to learn more about how ASICs can revolutionize your encoding workflow! https://netint.biz/3QQ49H8 Watch on YouTube: https://netint.biz/3QKnucQ #VPU #Quadra #streaming #broadcasting #videoProcessing

Understanding Stream vs Memory Mapped Interfaces 1. Stream Interfaces (AXI4-Stream) ✅Data Flow: Continuous and Unidirectional—Like YouTube Streaming. Imagine watching a live video on YouTube. The video data flows continuously from the server to your device, just like how stream interfaces allow data to flow from a source to a destination without interruption. Example: Sending a video feed from a camera sensor to a processing unit, similar to how a video stream reaches your device. ✅No Addressing Overhead is required: Just as you don’t need to "search" for the next part of a live stream, stream interfaces don’t need addresses. This makes them ideal for high-throughput applications. ✅Low Latency: Like how a smooth live stream has minimal delay, stream interfaces offer low-latency data transfer, perfect for real-time applications. Typical Use Cases: Video streaming, audio processing, and real-time sensor data processing. (Check out my ADC Data Processing using Streaming Interface Project). 2. Memory-Mapped Interfaces (AXI4, AXI4-Lite) ✅Data Access: Address-Based and Bidirectional Memory-mapped interfaces use specific addresses to access data, similar to how you access different videos or files by searching or navigating to a specific location. Example: Accessing a configuration register or reading/writing to a block of RAM. ✅Flexibility: These interfaces allow you to access any part of the memory or peripheral device you need, just like how you can watch any video on demand. ✅Higher Overhead: The need for addressing and control signals can introduce delays, similar to how searching for a video might take a bit more time compared to a live stream. ✅Typical Use Cases: Control registers, system memory access, and peripheral communication. ✅Which Interface to Choose? Stream Interface (AXI4-Stream) is like watching a live stream—ideal for continuous, high-speed data flow with low latency. Memory-Mapped Interface (AXI4, AXI4-Lite) is like searching for and playing a video on demand—better for tasks requiring flexibility and control. 🔍Key Takeaway Understanding stream and memory-mapped interfaces lets you design efficient FPGA systems. Whether you need the uninterrupted flow of a live stream or the precise control of on-demand access, choosing the right interface is key. #FPGA #EmbeddedSystems #AXI #AXIStream #AXIMemoryMapped #Xilinx #Vivado #YouTubeStreaming

🎥 The Video Processing Revolution: Decoding at Scale 🚀 Everyone wants efficient video encoding. That's why NETINT Technologies Inc. built the VPU category, achieving 320 1080p30 live AV1, HEVC, and H.264 broadcast quality encodes in a 1RU server. It's impressive. But what about decoding density? With cloud-based video analytics on the rise, decoding efficiency is in the spotlight. So, how do we manage thousands of video streams when GPUs are hungry for data, yet their video IP block occupies just 15% of the die? Our Tech Emmy Award-winning Quadra VPUs aren't just encoding powerhouses. They're decoding champions, supporting VP9, HEVC, and H.264. 🔢 The Numbers Speak for Themselves: • 64 channels of 1080p30 video = 47.8 Gbps total bitrate • One Quadra T2A or two T1Us split traffic across two chips • PCIe Gen 4 bandwidth utilization: Only 37% • PCIe Gen 5 bandwidth utilization: Drops to 18.5% The bottom line: NETINT Quadra VPUs with PCIe Gen 4 or Gen 5 servers unlock unprecedented decoding densities. #VideoProcessing #CloudComputing #NETINT #streamingmedia

The introduction of NPUs with our #Snapdragon compute platforms is a significant leap forward in the evolution of the PC space. With #AI-driven capabilities now at the core, we’re unlocking faster, more efficient processing while extending battery life, creating a powerful synergy between performance and endurance. This is just the beginning of how NPUs will redefine computing, setting the stage for next-gen experiences across productivity, creativity, and more. Learn more: #TeamQualcomm https://lnkd.in/eWMJjCe2

#SDR Means a lot of different things to a lot of different people. We recently released the Zmod SDR, which gives our Eclypse Z7 a tantalizing set of options when coupled with the Zmod AWG. Whitney Knitter breaks down how to set up this system (using #GNURadio) to make an FM audio transceiver. https://lnkd.in/gE3JKJnG #digitaldesign #engineering #softwaredefinedradio

In an interview with 8K Association, Sergio Ammirata, Ph.D., Chief Scientist at SipRadius, discussed the company’s advancements in AV1 live encoding with Intel Arc GPUs. Read the full article to discover how SipRadius is improving real-time encoding to enhance video compression efficiency, addressing bandwidth challenges, and achieving better encoding density: https://bit.ly/4e7VTLD #IntelArc #8KTechnology #VideoStreaming #AV1 #LiveBroadcasting #RemoteProduction #MediaTechnology #StreamingSolutions #Broadcast #Encoding #Transcoding

GPU has better data parallelism support and CPU does better task parallelism. With this in mind, a messaging system, pub-sub and streaming included, a new breed of messaging system should appear that’s GPU native. This is because messaging system is inherently acts on data parallelism and exhibits so. Do we have such a system? Anybody knows? Please don’t say Kafka on gpu :)

High speed, low drag

Explore the potential of combining Edge Impulse for model development with the NVIDIA DeepStream SDK. Learn more in our latest technical blog post. #NVIDIAInception

Explore the potential of combining Edge Impulse for model development with the NVIDIA DeepStream SDK. Learn more in our latest technical blog post. #NVIDIAInception