The document proposes a non-von-Neumann heap (NVNHeap) for better streaming, capturing, and storing of raw 8K video data. The NVNHeap would bypass RAM by writing video data directly from hardware sources into Intel Optane memory, which can be directly mapped to the memory bus. This would alleviate issues like increased synchronization latencies caused by using multiple RAM modules or SSDs. Simulation shows the NVNHeap approach could store hours of 8K video payload using just a few Optane devices to meet the required bandwidth. The approach aims to improve efficiency over von Neumann architectures by removing RAM from the write cycle and enabling real-time persistence without intermediate layers.

1. Non-von-Neumann Heap for
Better Streaming, Capturing
and Storing of Raw 8K Video
Data
Mohamed Shaafiee 1 and Rajasvaran Logeswaran 2
Asia Pacific University of Technology and Innovation
Technology Park Malaysia, Bukit Jalil, 57000 Kuala Lumpur, Malaysia
1 mail@shaafiee.com, 2 loges@ieee.org

3. 85 GB/s on most powerful
Xeon processors
1.9 GB/s
Single Xeon processor: up to 3TB [1]
6-way system to store 8K football match in RAM
Increases synchronization latencies
Inefficient power and CPU utilization
At least 9 SSDs on independent channels for 8K video bandwidth
Increases synchronization latencies
Can jam enough SSDs into a machine to store 90 min of 8K football

4. [2]
1.9 GB/s

5. • SSD cannot be mapped to memory bus
• Video sources are written into memory first
• Memory-mapped files reside in memory
• Contents synchronized with the non-volatile file
• Real-time persistence is impossible
• Variables in drivers are stored in RAM
• Intermediate write latency to RAM increases with
the increase in data

6. Resolution
Chroma
subsampling
for YUV[3]
Bandwidth
(GB/s)
90min raw
video
payload (TB)
4K UHD (3840x2160) 4:2:2 0.50 2.70
4K UHD (3840x2160) None 0.75 4.10
8K (8192x4320) 4:2:2 2.13 11.48
8K (8192x4320) None 3.18 17.15
16K (15260x8640) 4:2:2 7.93 42.80
16K (15260x8640) None 11.88 64.20
Table 1. Video payload at various resolutions (10 bits per YUV channel and
24 frames per second)

7. • Replace RAM with a non-volatile memory
• Implement non-von-Neumann architecture
• No intermediate layer in data acquisition or
processing
• Write directly to non-volatile memory

8. •Intel Optane memory[4]
• Can be mapped to the
memory bus
• Intel to introduce
specific chipsets and
processors
An Optane memory module
(www.intel.com)
ASRock motherboard with Optane support
(www.asrock.com)

9. • Create pointer to address in
the Optane range instead of
RAM
• Read from video source
directly into this pointer
• Thus bypass RAM
• Create multiple pointers to
addresses in multiple
devices to increase
bandwidth

10. • NVNHeap = non-von-Neumann heap
•Optane device hosts the heap
• Video data is written directly into it
• Optane device is non-volatile
• Thus, no explicit persistence is required
• RAM does not partake in the write cycle
• von Neumann architectures require memory

11. • Currently possible as a kernel module
• For contiguous access to physical memory
• Direct mapping using ioremap
• Create a pointer to this address range
void *addr;
addr = optaneAddress;
• Assign data directly to this pointer
*addr = hardware.getData();

12. Resolution
Chroma
subsampling
Raw data bandwidth
in GB/s
No. of Optane
devices needed
4K 4:2:2 0.50 1
4K None 0.75 1
8K 4:2:2 2.13 2
8K None 3.18 2
16K 4:2:2 7.93 4
16K None 11.80 6
Table 2. No of Optane devices needed to yield the required bandwidth
(10 bits per YUV channel and 24 frames per second)

13. • NVNHeap has been successfully simulated in SSD
• Only simulated – stacks are still maintained in RAM
• Awaiting the arrival of Optane to market
• A non-von-Neumann VM is currently being tested
• Native user-space NVNHeaps

14. [1] Intel, “Intel® Xeon® Processor E7-8893 v4 (60M Cache, 3.20 GHz)
Specifications,” Intel® ARK (Product Specs), 06-Jun-2016. [Online]. Available:
http://ark.intel.com/products/93791/Intel-Xeon-Processor-E7-8893-v4-60M-
Cache-3_20-GHz. [Accessed: 25-Jun-2016].
[2] J. Edwards and S. O’Keefe, “Eager recirculating memory to alleviate the von
Neumann Bottleneck,” in 2016 IEEE Symposium Series on Computational
Intelligence (SSCI), 2016, pp. 1–5.
[3] C. H. Lin, K. L. Chung, and C. W. Yu, “Novel Chroma Subsampling Strategy
Based on Mathematical Optimization for Compressing Mosaic Videos With
Arbitrary RGB Color Filter Arrays in H.264/AVC and HEVC,” IEEE Trans. Circuits
Syst. Video Technol., vol. 26, no. 9, pp. 1722–1733, Sep. 2016.
[4] A. Kingsley-Hughes, “Can Optane SSD DC P4800X allow Intel to keep its lead
in the server storage market?,” ZDNet. [Online]. Available:
http://www.zdnet.com/article/can-optane-ssd-dc-p4800x-allow-intel-to-
keep-its-lead-in-the-server-storage-market/. [Accessed: 17-Apr-2017].

15. Questions?