The Codensity G4 was the first encoding ASIC developed by NETINT. There are two G4-based transcoders, the T408 (Figure 1), is available in a U.2 form factor and as an add-in card, and the T432 (Figure 2), which is available as an add-in card. The T408 contains a single G4 ASIC and draws 7 watts under full load, while the T432 contains four G4 ASICs and draws 27 watts.
If you’re buying your own host, the selected CPU should reflect the extent of processing that it needs to perform and the overhead requirements of the media processing framework that is running the transcode function.
When transcoding inputs without scaling, as in a cloud gaming or conferencing application, a modest CPU can suffice. If you are creating standard encoding ladders, deinterlacing multiple streams, or frequently scaling incoming videos, you’ll need a more capable CPU. For a turn-key solution, check out the NETINT Video Transcoding Server options.
The NETINT Video Transcoding Server includes ten T408 U.2 transcoders. It is targeted for high-volume transcoding applications as an affordable turn-key replacement for existing hardware transcoders or where a drop-in solution to a software-based transcoder is preferred.
The base model costs $7,000 and is built on the Supermicro 1114S-WN10RT server platform powered by an AMD EPYC 7232P CPU Series Processor with eight CPU cores and 16 threads running Ubuntu 20.04.05 LTS. The server ships with 512 GB of DDR4-3200 RAM and a 400GB M.2 SSD drive with 3x PCIe slots and ten NVME slots that house the ten T408 transcoders. At full transcoding capacity, the server draws 220 watts while encoding or transcoding up to ten 4Kp60 streams or as many as 160 720p60 video streams.
The server is also offered with two more powerful CPUs, the AMD EPYC 7543P Server Processor (32-cores/64-threads, $8,900) and the AMD EPYC 7713P Server Processor (64-cores/128-threads, $11,500). Other than the CPU, the hardware specifications are identical.
All Codensity G4-based products support HDR10 and HDR10+ for H.264 and H.265 encode and decode, as well as EIA CEA-708 closed captions for H.264 and H.265 encode and decode. In low-latency mode, all products support sub-frame latency. Other features include region-of-interest encoding, a customizable GOP structure with eight presets, and forced IDR frame inserts at any location.
The T408, T432, and NETINT Server are targeted toward high-volume interactive applications that require inexpensive, low-power, and high-density transcoding using the H.264 and HEVC codecs.
In addition to roughly quadrupling the H.264 and HEVC throughput of the Codensity G4, the Codensity G5 is our second-generation ASIC that adds AV1 encode support, VP9 decode support, onboard scaling, cropping, padding, graphical overlay, and an 18 TOPS (Trillions of Operations Per Second) artificial intelligence engine that runs the most common frameworks all natively in silicon.
Codensity G5 also includes audio DSP engines for encoding and decoding audio codecs such as MP3, AAC-LC, and HE AAC. All this on-board activity minimizes the role of the CPU allowing Quadra products to operate effectively in systems with modest CPUs.
Where the G4 ASIC is primarily a transcoding engine, the G5 incorporates much more onboard processing for even greater video processing acceleration. For this reason, NETINT labels Codensity G4-based products as Video Transcoders and Codensity G5-based products as Video Processing Units or VPUs.
The Codensity G5 is available in three products (Figure 4), the U.2-based Quadra T1 and PCIe-based Quadra T1A, which include one Codensity G5 ASIC, and the PCIe-based , which includes two Codensity G5 ASICs. Pricing for the T1 starts at $1,500.
In terms of power consumption, the T1 draws 17 Watts, the T1A 20 Watts, and the T2 draws 40 Watts.
All Codensity G5-based products provide the same HDR and close caption support as the Codensity G4-based products. They have also been tested on Windows, MacOS, Linux and Android OS with support for virtual machine and container virtualization, including Single Root I/O Virtualization [SRIOV].
From a quality perspective, the Codensity G4-based transcoder products offer no configuration options to optimize quality vs. throughput. Quadra Codensity G5-powered VPUs offer features like lookahead and rate-distortion optimization that allow users to customize quality and throughput for their particular applications.
Beyond VP9 ingest and AV1 output, and superior on-board processing, the Codensity G5 AI engine is a game changer for many current and future video processing applications. Each Codensity G5 ASIC includes two onboard Neural Processing Units (NPUs). Combined with Quadra’s integrated decoding, scaling, and transcoding hardware, this creates an integrated AI and video processing architecture that requires minimal interaction from the host CPU.
Today, in early 2023, the AI-enabled processing market is nascent, but Quadra already supports several applications like AI-based region of interest filter, background removal (see Quadra App Note APPS553), and others. Additional features under development include an automatic facial ID for video conferencing, license plate detection and OCR for security, object detection for a range of applications, and voice-to-text.
Like NETINT’s Codensity G4 based products, Quadra VPUs are ideal for interactive applications that require low CAPEX and OPEX. Quadra VPUs offer increased onboard processing that enables lower-cost host systems and the ability to customize throughput and quality, deliver AV1 output, and deploy AI video applications.
In the FFmpeg implementation, the libavcodec patch on the host server functions between the NETINT hardware and FFmpeg software layer, allowing existing FFmpeg-based video transcoding applications to control hardware operation with minimal changes.
The NETINT hardware device driver software includes a resource management module that tracks hardware capacity and usage load to present inventory and status on available resources and enable resource distribution. User applications can build their own resource management schemes on top of this resource pool or let the NETINT server automatically distribute the decoding and encoding tasks.
In automatic mode, users simply launch multiple transcoding jobs, and the device driver automatically distributed the decode/encode/processing tasks among the available resources. Or, users can assign different hardware tasks to different NETINT devices, and even control which streams are decoded by the host CPU or NETINT hardware. With these and similar controls, users can most efficiently balance the overall transcoding load between the NETINT hardware and host CPU and maximize throughput.
That’s the overview. For more information on any product, please check the following product pages (click the image below to see product page).
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