NVIDIA’S TEGRA K1 SYSTEM-ON-CHIP€¦ · NVIDIA’S TEGRA K1 SYSTEM-ON-CHIP . Tegra K1 Battery...
Transcript of NVIDIA’S TEGRA K1 SYSTEM-ON-CHIP€¦ · NVIDIA’S TEGRA K1 SYSTEM-ON-CHIP . Tegra K1 Battery...
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Michael Ditty, Tegra Architecture
Co-authors: John Montrym, Craig Wittenbrink
NVIDIA’S TEGRA K1 SYSTEM-ON-CHIP
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Tegra K1
Battery Saver Core
2x ISP
ARM7 2160p30 VIDEO
ENCODER
2160p30 VIDEO
DECODER AUDIO
USB 3.0
SECURITY ENGINE
HDMI Dual
DISPLAY UART
MIPI DSI/CSI/
HSI
E,MMC 4.5
DDR3L LPDDR2 LPDDR3
SPI SDIO
I2S I2C
Kepler
GPU Kepler GPU (192 CUDA Cores)
Open GL 4.4, OpenGL ES3.1+AEP, DX12, CUDA 6
CPU
Quad Core Cortex A15 “r3”
With 5th Battery-Saver Core; 2MB L2 cache
OR
Dual Denver CPU
CAMERA Dual High Performance ISP
1.2 Gigapixel throughput, 100MP sensor
POWER Lower Power
28HPM, Battery Saver Core
DISPLAY 4K panel, 4K HDMI
DSI, eDP, LVDS, High Speed HDMI 1.4a
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Overview
Kepler into Mobile
Tegra ISP
Power Management
Mobile Enablement
Demo Intro
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A Major Discontinuity in Mobile Graphics
ES3.1+AEP, OGL4.4, DX12 Tessellation, Compute Shaders,
ASTC, GPGPU
ES2.0, DX9 Programmable Pixel
Shaders
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Mobile Roadmap Meets GeForce
MOBILE ARCHITECTURE
Maxwell
Kepler
Tesla
Fermi
Tegra 3
Tegra 4
Tegra K1
GEFORCE ARCHITECTURE
Advancem
ents
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Tegra K1
Metric Tegra 4 Tegra K1 Units
FP32 ops 48 384 Per clock
Z-only Primitives 0.1 1 Per clock
Zcull - 256 Pixels/clk
Raster 8 64 Samples/clk
Texture 4 8 Bilinear filters/clk
ZROP 8 64 Samples/clk
L2 size 32 128 KBytes
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L2 Cache
GigaThread Engine
Memory Interface
ROP ROP ROP ROP
GPC
SMX
Raster Engine
Polymorph Engine 2.0
Tegra K1 / Kepler
Graphics Core Architecture 192 CUDA cores
Unified Memory Cache
Dedicated Accelerators Geom / Tessellation
Z Cull
Z / Color ROP
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Power Efficiency
Clock and power gating Multi-level Clock Gating
Power Gating
Rail Gating
Architectural power improvements Interconnect and Data Paths architected for mobile
Shader Bypass
GPU L2 Cache and Compression
Work reduction Aggressive Culling Of Z, Stencil, Attribute Fetch
Early Z
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DuaI Next Gen ISP
Performance 1.2Gp total pixel throughput
600Mp each ISP
4096 simultaneous focus points
14 bits input
100Mp camera support
Interoperability Reconfigurable ISP fabric
Full GPGPU interoperability
Memory or Isochronous sourcing
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Tegra K1 Computational Photography Architecture
Kernels
GPU
ISP-B
XA
MI
ATO LS NR LAC0 AP
H1 FB AT1 DS FX
XB
ISP-A
XA
MI
ATO LS NR LAC0 AP
H1 FB AT1 DS FX
XB
Kernels
CPU Kernels
CPU Kernels
GPU
Frame/Image Bus
State Bus
K0
K1
Kn
K0
K1
Kn
K0
K1
Kn
K0
K1
Kn
VI-
Mux
F0 F1 Fn
S0 S1 Sn
CSI
GPU + ISP + CPU
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GPU Power Management
Pow
er
Usa
ge
Time
GPU Idle State Transitions
Active
Idle Transition
Power gating
Rail gating
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Multi-core Gaming
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Multi-core Gaming
CPU
Kepler GPU
Video
Encode
Video
Decode
ISP ISP
Display
64bit
DRAM
Audio
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Multi-core gaming power management
Balance power & performance
across cores and power rails.
Clocking policies must look at
more than active time.
Power optimization must be
done globally, not locally to
each unit.
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Multi-core video processing “Live” Local Tone Mapping
Original
LTM
Kepler GPGPU Processing
30fps
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CPU
Kepler GPU
Video
Encode
Video
Decode
ISP ISP
Display
64bit
DRAM
Audio
Multi-core video processing
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Multi-core video processing
Utilize burst performance for latency reduction
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Tegra K1 Benchmarks
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
GFXBench 3.0Manhattan
GFXBench 3.0Trex-HD
AndEBench-Pro
Perf
orm
ance r
ela
tive t
o
Fast
est
Com
peti
tor
Shield Tablet
Competitor X
Shield Portable
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Scalability Across Platforms
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Mobile Compute
NV JETSON
Tango Tablet Automotive Computer Vision
CUDA
VisionWorks Toolkit
Renderscript
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Tegra K1 Compute Benchmark
0
1
2
3
4
5
6
7
Compubench RS (Geometric Mean)
Perf
orm
ance r
ela
tive t
o F
ast
est
C
om
peti
tor
Shield Tablet
Competitor X
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Consumer Devices
Xiaomi MiPad Shield Tablet
Acer Chromebook 13
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NVIDIA Dabbler Improving the user experience with Tegra K1
Watercolor
GPGPU simulates realistic water
Oil painting
3D modeling enables realistic
lighting
Low Pen-to-Ink latency
Optimized GPU rendering paths to
reduce latency.
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Conclusion
New capabilities in mobile
Compute, OpenGL 4.4, Advanced Imaging Pipeline
Great performance
Over 2x the performance of current mobile devices
Enabling new platforms and ecosystems
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Acknowledgment
We would like to thank the GPU & Tegra teams across NVIDIA who
collaborated to make this chip possible.