SDPA Technology

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Hybrid Stochastic-Deterministic
Video Processing Architecture

Revolutionary technology combining low-power approximate computing with precision GPU refinement for efficient, high-quality video processing in mobile, IoT, and consumer devices

SDPA = Stochastic-Deterministic Processing Architecture

The Challenge

Traditional video processing systems face critical inefficiencies in power consumption, precision allocation, and adaptability

Traditional Processing

  • High Energy Consumption: Deterministic arithmetic for every pixel
  • Over-Precision: Processing smooth regions with same precision as complex edges
  • Fixed Trade-offs: No runtime quality-performance adaptation
  • Bandwidth Inefficiency: No confidence metadata for selective refinement

Hybrid SDPA Solution

  • 60-70% Energy Savings: Stochastic Computing (SC) for smooth regions via SCA with thermal noise entropy sources
  • Smart Allocation: Precision processing via GRE only where needed (15% of pixels) based on confidence maps from SPB
  • Dynamic Adaptation: Real-time quality-performance optimization through feedback controller adjusting SCA/GRE balance
  • Confidence-Guided: Uncertainty metadata in PMF drives intelligent computational resource allocation decisions

The Innovation

ESSENCE OF THE INVENTION

Traditional video systems treat every pixel as equally certain and spend the same amount of compute everywhere. Our invention flips that model: the signal is encoded and processed as probability first, not as fixed numbers. A stochastic engine produces a fast, low-power approximation for the whole frame, while a deterministic engine is invoked only where the system is uncertain. Confidence (variance) travels with the data, so computation is allocated by need—edges, motion, text, and fine detail get more precision; stable regions do not.

  • Probabilistic Media Format (PMF): carries mean + variance (and correlation) so the decoder knows where certainty is high or low.
  • Two-path compute: a stochastic pass handles most of the image at very low power; a precision pass corrects only low-confidence regions.
  • Closed-loop control: live confidence maps drive selective refinement to hit target quality within power/latency budgets.
  • Progressive deployment: works in software today, accelerates on FPGA/DSP, and reaches maximum efficiency on dedicated SoCs.

The result is a quality–efficiency breakthrough: up to broadcast-grade visual quality with far fewer deterministic operations, enabling lower power, lower latency, and scalable performance from phones to automotive vision.

KEY TERMINOLOGY

PMF

Probabilistic Media Format – Novel container encoding video as statistical distributions (mean, variance, correlation)

SCA

Stochastic Compute Array – Low-power processing that operates on random bitstreams to estimate results quickly

SPB

Shared Probabilistic Buffer – Interface/memory carrying approximate values with confidence for selective refinement

GRE

GPU Refinement Engine – Deterministic processing applied only to low-confidence regions to recover precision

Cost Reduction

60-70%
Energy Reduction vs. Full Deterministic Processing
2-5×
Latency Improvement for Equivalent Quality
85%
Pixels Processed via Low-Power SC
15%
Pixels Requiring GPU Refinement

Visual Processing Comparison

See the difference between traditional deterministic processing and our hybrid SDPA approach

Traditional Processing

Power Usage
100%
Time
0ms

SDPA Hybrid Processing

Power Usage
35%
Time
0ms
GPU Regions
0%

How It Works:

Traditional Processing: Every pixel processed with full deterministic precision, consuming maximum power and time.

SDPA Processing: Rapid stochastic computing (SC) processes most pixels at high speed with low power (green). The system then identifies uncertain regions (edges, textures - shown in yellow) and applies selective GPU refinement (bright green), achieving dramatic energy savings while maintaining quality.

System Architecture Flow

Real-time data flow from PMF encoder through transmission to hybrid SDPA decoder

PMF → SCA → SPB → GRE → Display Output

Data Flow: PMF demultiplexer parses incoming stream → SCA performs rapid stochastic reconstruction → SPB stores results with confidence metrics → GRE selectively refines high-uncertainty regions → Final composited output to display

Processing Pipeline

1

PMF Reception & Parsing

Receive frame block, parse header, demultiplex channels (mean, variance, correlation, correction)

2

Stochastic Bitstream Generation (SCA)

SCA loads mean values and configures entropy sources (thermal noise resistor or LFSR pseudo-random generator) with correlation parameters from PMF metadata

Analog Thermal Noise Source (Preferred Entropy Source for SCA)

The SCA uses physical entropy sources to generate random bitstreams for stochastic computing. The preferred implementation uses a 100 kΩ resistor whose Johnson-Nyquist thermal noise is amplified 500× and compared against mean pixel values to produce truly random bitstreams at 1-4 GHz with <100 µW power consumption per generator.

100 kΩ Resistor
Johnson-Nyquist Thermal Noise
Power: <100 µW

Noise Output Waveform

Random bitstream from thermal noise
Amplified 500× → Comparator → 1-4 GHz

3

Stochastic Computation

Arithmetic units perform video operations (color conversion, filtering) in stochastic domain - 64-256 pixels processed in parallel

4

Stochastic-to-Binary Conversion

Accumulators integrate bitstreams into binary values, compute confidence metrics, transfer to Shared Probabilistic Buffer

5

Confidence Map Synthesis

SPB combines SCA convergence metrics with PMF variance map: Confidence = w₁·(1-Var_PMF) + w₂·(1-Var_SC) + w₃·smoothness

6

Conditional GPU Dispatch

If high-uncertainty detected → GPU applies edge-aware filters and deterministic corrections to identified tiles

7

Output Composition

Blend SC approximate values with GPU corrections using confidence-weighted spatial blending with smooth transitions

8

Feedback Loop

Monitor quality metrics, power consumption, and latency → dynamically adjust parameters for subsequent frames

Pipeline Processing Stream

Interleaved operation demonstrating parallel processing stages

Deployment Strategies

Three-tier deployment model enabling gradual market adoption from existing devices to future dedicated hardware

Tier 1

Software-Only Fallback

For Existing Devices

Immediate deployment on current hardware using GPU compute shaders to emulate stochastic operations

  • Works on any device with modern GPU
  • No hardware changes required
  • Format compatibility proven
  • Enables content distribution now
  • Performance comparable to traditional decoding

Use Cases: Immediate deployment, content distribution, format validation, testing

Tier 2

Accelerated Hybrid

Near-Term Devices (2026-2027)

Programmable FPGAs or DSPs implement partial SCA functionality for measurable benefits

  • FPGA/DSP-based stochastic processors
  • Digital pseudo-random generation (LFSR)
  • GPU refinement for complex regions
  • 20-40% power reduction
  • 1.5-2× latency improvement

Use Cases: High-end smartphones, professional video equipment, development platforms

Tier 3

Full Hardware Implementation

Future Dedicated SoCs (2027+)

Custom ASIC with analog entropy sources realizes full potential of the architecture

  • Dedicated SCA with thermal noise sources
  • Minimal GPU intervention required
  • 60-70% power reduction
  • 3-5× latency improvement
  • Optimal quality-efficiency balance

Use Cases: Next-gen mobile devices, IoT cameras, edge processors, automotive vision

Progressive Enhancement Model

No immediate hardware requirement for deployment - the format works on existing devices in software mode, with progressive performance improvements as purpose-built hardware becomes available

Conformance & Quality Assurance

Statistical compliance and reproducibility bounds ensure consistent quality across implementations

Statistical Compliance

PMF decoders guarantee outputs within specified statistical bounds:

  • Mode 1: Pixel values within ±1 LSB with 99.9% probability
  • Mode 2: Pixel values within ±0.5 LSB with 99.99% probability
  • Validation: Statistical testing over reference sequences

Quality Metrics

Specialized metrics for probabilistic reconstruction:

  • P-PSNR: Probabilistic PSNR over statistical distributions
  • Confidence-Weighted SSIM: Structural similarity with confidence weighting
  • Perceptual Assessment: Focus on human-visible differences

Test Vectors

Standardized validation sequences:

  • Synthetic Patterns: Gradients, edges, frequency sweeps
  • Natural Sequences: Diverse real-world content
  • Pathological Cases: Stress tests for error handling

Patent Claims Overview

🔒 Patent Pending

Portuguese Patent Application Filed
INPI Portugal Application No. 20252007422859
Filing Date: October 18, 2025 | Reference: 097 605 864

International patent protection strategy (PCT/EPO/US) available upon partnership or investment.
Priority date established. All rights reserved under applicable intellectual property law.

45 patent claims covering system architecture, methods, and applications — click any claim for details

Claim # Title
Claim 1 Hybrid Processing System
Claim 2 Processing Method
Claim 3 Complete SDPA System
Claim 4 Encoding Method
Claim 5 Decoding Method
Claim 6 Entropy Source
Claim 7 PRNG Integration
Claim 8 Correlation Control
Claim 9 Confidence Metrics
Claim 10 Selective Refinement
Claim 11 Controller Policies
Claim 12 Color/Matrix Operations
Claim 13 Nonlinear Filters
Claim 14 Motion/Flow
Claim 15 Neural Layers
Claim 16 SPB Layout
Claim 17 PMF Compression
Claim 18 Temporal Consistency
Claim 19 Correction Channels
Claim 20 Quality Targets
Claim 21 DVFS / Power Gating
Claim 22 Clock Domains
Claim 23 Deterministic Fallback
Claim 24 Hybrid FPGA/DSP
Claim 25 ASIC Realization
Claim 26 Entropy Validation
Claim 27 Security/DRM
Claim 28 HDR/Colorspaces
Claim 29 Audio/Multiband
Claim 30 Multispectral
Claim 31 Edge/IoT Mode
Claim 32 Tiling/Worklists
Claim 33 Asynchronous Blend
Claim 34 Latency Modes
Claim 35 Streaming Adaptation
Claim 36 Scene Semantics
Claim 37 Error Resilience
Claim 38 Training/Adaptation
Claim 39 Calibration
Claim 40 Quality Telemetry
Claim 41 Power Telemetry
Claim 42 Developer Hooks
Claim 43 Compatibility
Claim 44 Authoring Tools
Claim 45 Use-Case Profiles

Claim 1: Hybrid Processing System

System with probabilistic media format, stochastic compute, and deterministic refinement.

  • Media encoded with uncertainty/variance to guide work
  • Stochastic array performs fast approximate operations
  • Refinement engine corrects low-confidence regions
  • Controller balances quality, latency, and power

Claim 2: Processing Method

Method that produces output by combining approximate and refined results.

  • Receive PMF data with means/variance/correlation
  • Run stochastic pass to produce fast approximations
  • Detect uncertain tiles; selectively refine them
  • Composite to final frame with confidence weights

Claim 3: Complete SDPA System

Defines PMF, SCA, SPB, GRE, and a feedback loop as an integrated pipeline.

  • PMF: mean, variance, correlation, optional corrections
  • SCA: stochastic MACs with controlled correlation
  • SPB: buffer for approx values + confidence
  • GRE: deterministic correction of masks
  • Controller: thresholds, budgets, telemetry

Claim 4: Encoding Method

Encoder generates probabilistic fields and metadata for decoding guidance.

  • Compute mean and variance per pixel/block
  • Derive correlation control across regions/frames
  • Include optional correction data for hard cases
  • Compress and pack into PMF container

Claim 5: Hybrid Decoding Method

Decoder combines stochastic estimation with targeted deterministic passes.

  • Seed random sources; generate bitstreams
  • Perform stochastic arithmetic on streams
  • Compute confidence from variance/convergence
  • Apply GRE to masked regions; compose output

Claim 6: Entropy Source

True-random entropy via thermal noise front-end for stochastic streams.

  • Resistor noise amplified and thresholded
  • Comparator outputs unbiased random bits
  • Supports many parallel sources at low power

Claim 7: PRNG Integration

Deterministic PRNGs provide scalable, reproducible stochastic streams.

  • LFSR/XORShift seeded by TRNG or fixed seeds
  • Per-tile/per-layer stream independence
  • Deterministic replay for debugging/tests

Claim 8: Correlation Control

Metadata dictates where streams are shared vs. decorrelated.

  • Local vs. global correlation selection
  • Prevents bias; preserves structure
  • Improves SCA accuracy/variance

Claim 9: Confidence Metrics

Combine encoder variance with runtime convergence into confidence maps.

  • Bit-length, variance, residual error
  • Adaptive thresholds per content
  • Masks drive GRE worklists

Claim 10: Selective Refinement

Refinement engine processes only low-confidence tiles/edges/motion.

  • Masks derived from metrics and content
  • GRE applies precise arithmetic
  • Reduces overall compute/power

Claim 11: Controller Policies

  • Set targets for quality/latency/power
  • Dynamic thresholds and budgets
  • Uses live telemetry to adjust masks

Claim 12: Color/Matrix Operations

  • Matrix MACs via stochastic multiply/add
  • Color space conversion in SCA
  • Early-stop on convergence

Claim 13: Nonlinear Filters

  • Bilateral/guided/NLM in SC domain
  • Variance-aware weights
  • Refine only hard regions

Claim 14: Motion and Flow

  • Stochastic correlation for motion
  • Temporal masks prioritize motion edges
  • GRE resolves ambiguous vectors

Claim 15: Neural Layers

  • Stochastic inference accelerators
  • Super-resolution/denoise blocks
  • Selective high-precision fallback

Claim 16: SPB Layout

  • Approx values + variance in one buffer
  • Coalesced access for GRE
  • Low-overhead mask transport

Claim 17: PMF Compression

  • Entropy coding matched to fields
  • Preserves uncertainty fidelity
  • Metadata for decoder control

Claim 18: Temporal Consistency

  • Smooth variance across frames
  • Reduce flicker in SCA output
  • Motion-aware smoothing

Claim 19: Correction Channels

  • Optional deltas/kernels per region
  • GRE applies guided corrections
  • Compact side data in PMF

Claim 20: Quality Targets

  • Track PSNR/SSIM/VMAF
  • Meet targets under power cap
  • Adaptive mask aggressiveness

Claim 21: DVFS / Power Gating

  • Scale clocks/voltage by load
  • Gate idle stochastic lanes
  • Mask density informs power

Claim 22: Clock Domains

  • Safe CDC for SCA↔GRE
  • FIFOs/synchronizers
  • Glitch-free handoff

Claim 23: Deterministic Fallback

  • Pure software mode
  • GPU compute emulation
  • Same format/bitstreams

Claim 24: Hybrid FPGA/DSP

  • Partial SCA in programmable logic
  • GRE on GPU/CPU
  • Noticeable power/latency gains

Claim 25: ASIC Realization

  • Dedicated SCA blocks
  • Analog entropy sources
  • Minimal GRE intervention

Claim 26: Entropy Validation

  • Online health tests
  • Statistical validation suites
  • Failover to PRNG

Claim 27: Security / DRM

  • Isolated entropy paths
  • Secure seeds/keys
  • Content protection aware

Claim 28: HDR & Colorspaces

  • Linear/log/HDR handling
  • Variance-aware tone mapping
  • WCG-safe transforms

Claim 29: Audio / Multiband

  • Probabilistic audio bands
  • Selective refinement of transients
  • Unified PMF carriage

Claim 30: Multispectral Support

  • NIR/thermal/depth channels
  • Variance for sensing noise
  • Cross-channel masking

Claim 31: Edge / IoT Mode

  • SCA-first low-power path
  • Small GRE footprint
  • Meets edge latency

Claim 32: Tiling & Worklists

  • Tile queues from masks
  • Batch-friendly for GRE
  • Great for GPUs

Claim 33: Asynchronous Compositing

  • Blend refined tiles on the fly
  • No need to stall SCA
  • Lower pipeline latency

Claim 34: Latency Profiles

  • Low-latency vs. high-quality
  • Adjust mask thresholds
  • Budget-aware control

Claim 35: Streaming Adaptation

  • Bitrate-variance coupling
  • Network-aware PMF
  • Smooth quality under bandwidth

Claim 36: Scene Semantics

  • AI finds important content
  • Priority refinement (faces/text)
  • Content-driven masks

Claim 37: Error Resilience

  • Variance helps conceal loss
  • Selective re-generation
  • Robust streaming

Claim 38: Training / Adaptation

  • Online tuning of seeds/correlation
  • Per-scene parameter updates
  • Improved efficiency over time

Claim 39: Calibration

  • Factory characterization
  • Runtime entropy health
  • Stable stochastic behavior

Claim 40: Quality Telemetry

  • Expose per-frame metrics
  • Controller feedback loop
  • Debug/analytics hooks

Claim 41: Power Telemetry

  • Measure and budget power
  • Refinement tied to budget
  • Energy-aware quality

Claim 42: Developer Overrides

  • APIs to set masks/thresholds
  • Force refine/skip regions
  • Profile and export

Claim 43: Compatibility Modes

  • Fallback to deterministic codecs
  • Graceful degradation
  • Interoperable containers

Claim 44: Authoring / Tools

  • Encoders/viewers with live variance
  • Mask visualization
  • Export presets

Claim 45: Use-Case Profiles

  • Mobile, edge, broadcast, AR/VR, automotive
  • Pre-set power/latency/quality curves
  • Consistent deployment behavior

Prior Art & Novelty Analysis

✓ No Direct Conflicts Found

Comprehensive Patent Database Search Completed

Extensive search of patent databases (Google Patents, USPTO, EPO) for keywords including "stochastic computing + video processing," "probabilistic encoding," "hybrid stochastic deterministic," and "probabilistic media format" revealed no patents that directly conflict with or fully anticipate this invention. The core novelty—integration of PMF with mean/variance/correlation metadata, hybrid SCA+GPU architecture, and dynamic feedback control based on confidence metrics—remains unique.

Related prior art and complementary research identified — click any row for detailed analysis

Reference Title Date Status
US20180204131A1 Stochastic Computation Using Pulse-Width Modulated Signals Jan 13, 2017 No Conflict
US11275563B2 Low-Discrepancy Deterministic Bit-Stream Processing Using Sobol Sequences Jun 21, 2019 No Conflict
US10996929B2 High Quality Down-Sampling for Deterministic Bit-Stream Computing Mar 15, 2018 No Conflict
US20230379469A1 Image Compression and Decoding Using Probabilistic Neural Networks Apr 27, 2020 No Conflict
Lee (2024) Multiplexer as MAC Operator for Stochastic Computing 2024-2025 Complementary

Key Differentiators

Probabilistic Media Format (PMF)

No prior art includes a custom media format with mean/variance/correlation metadata for uncertainty-driven processing

Hybrid Architecture

Unique integration of SCA for approximate processing with GPU refinement for high-uncertainty regions

Dynamic Feedback Control

Real-time confidence-based allocation of computational resources - not found in any prior art

US20180204131A1: Stochastic Computation Using PWM Signals

Status: No Conflict

Inventors: Mohammadhassan Najafi et al.

Priority Date: January 13, 2017

Key Technology:

  • Uses deterministic PWM signals (time-encoded pulses) for stochastic computing operations
  • Applies SC to video processing for low-power edge detection and gamma correction
  • Mentions hybrid deterministic aspects to reduce uncertainty from random SC fluctuations

Why No Conflict:

  • No probabilistic media format with mean/variance channels or correlation metadata
  • No GPU refinement or dynamic allocation based on confidence metrics
  • Focuses on hardware efficiency for general SC, not video-specific encoding/decoding with uncertainty-driven hybrid processing
  • Does not describe a complete system architecture like SDPA

US11275563B2: Low-Discrepancy Deterministic Bit-Stream Processing

Status: No Conflict

Inventors: Mohammadhassan Najafi et al.

Priority Date: June 21, 2019

Key Technology:

  • Deterministic stochastic computing using quasi-random Sobol sequences for bit-stream generation
  • Enables hybrid stochastic-binary convolution in neural networks for video feature extraction
  • Addresses uncertainty tolerance in video applications with low error rates via truncation

Why No Conflict:

  • Lacks a custom media format or uncertainty metadata (variance)
  • No SCA with GPU refinement or feedback controller
  • More about general SC acceleration for CNNs, not a full video processing system with probabilistic encoding
  • Does not include confidence-driven selective refinement

US10996929B2: High Quality Down-Sampling for Deterministic Bit-Stream Computing

Status: No Conflict

Inventors: Mohammadhassan Najafi, David J. Lilja

Priority Date: March 15, 2018

Key Technology:

  • Pseudo-random deterministic SC with LFSRs for bit-streams
  • Applied to video edge detection (Robert's cross algorithm)
  • Hybrid deterministic-pseudo-random for better convergence and energy savings
  • Tolerates some uncertainty measured as mean absolute error

Why No Conflict:

  • No mention of probabilistic media formats, correlation metadata, or variance-based confidence
  • No dynamic refinement engine (e.g., GPU for high-uncertainty regions)
  • Primarily hardware optimization for SC in image/video tasks, not a complete encoding/decoding architecture
  • Does not include the SPB, GRE, or feedback controller components

US20230379469A1: Image/Video Compression Using Probabilistic Neural Networks

Status: No Conflict

Inventors: Chri Besenbruch et al.

Priority Date: April 27, 2020

Key Technology:

  • Lossy video compression using probabilistic neural network models for latent representations
  • Entropy coding with potential uncertainty in predictions
  • AI-based compression approach (e.g., variational autoencoders)

Why No Conflict:

  • Focuses on AI-based compression, not stochastic computing or hybrid stochastic-deterministic hardware
  • No SCA, GPU refinement, or explicit variance/correlation in a PMF-like format
  • More about general probabilistic modeling for codecs, not the specific SDPA system
  • Different technological approach and architecture

Complementary Research: Multiplexer as MAC Operator

Status: Complementary (Not Patented)

Researcher: Yang Yang Lee, Universiti Sains Malaysia

Publication: 2024-2025 (Academic Research)

Recent academic research demonstrates how multiplexers can function as scalable multiply-accumulate (MAC) operators in the stochastic computing domain. This technique is complementary to the SDPA architecture and could be integrated as an implementation detail within the Stochastic Compute Array (SCA) component.

Lee's Contribution

  • Focus: Hardware optimization for AI acceleration using stochastic computing
  • Innovation: Reimagines multiplexers as scalable MAC operators (MUX-8, MUX-16, MUX-32)
  • Application: General AI hardware (CNNs, neural networks on FPGAs)
  • Status: Published research, not patented

Relationship to SDPA

  • Complementary: Can be integrated into SCA implementation (Claim 9)
  • Non-Conflicting: Lee's work focuses on MAC; SDPA is complete video system
  • Validation: Demonstrates viability of stochastic computing
  • Enhancement: Could improve SCA efficiency

Key Insight

Lee's research validates the foundational principles of stochastic computing for efficient hardware operations. His MUX-based MAC technique represents a potential implementation option for our Stochastic Compute Array, but does not encompass the broader SDPA architecture, Probabilistic Media Format, uncertainty-driven GPU refinement, or dynamic feedback control that define our invention.

✓ No Patent Filed — Research is in the Public Domain

This complementary technique strengthens the case for stochastic computing applications

Academic Publications:

ResearchGate Profile →

Beyond Video: Extended Applications

The probabilistic encoding concept extends naturally to audio, multispectral imaging, and scientific data

♪

Audio Processing (PWAV)

Probabilistic Waveform Format

  • Mean waveform + variance envelope
  • Spectral/temporal correlation structure
  • Stochastic filtering and effects
  • 50-60% power savings

Ideal for: Voice assistants, hearing aids, wireless earbuds, always-on audio

◈

Hyperspectral Imaging

Remote Sensing & Scientific Applications

  • Each wavelength band encoded probabilistically
  • Variance represents measurement uncertainty
  • Native uncertainty-aware processing
  • Efficient for satellite/drone systems

Applications: Agriculture monitoring, mineral exploration, environmental science

✚

Medical Imaging

CT, MRI, Ultrasound Processing

  • Variance maps represent measurement confidence
  • Uncertainty-aware diagnostic algorithms
  • Noise characteristics natively encoded
  • Enhanced clinical decision support

Benefit: Clinicians see both images and confidence levels for better diagnosis

◉

Scientific Visualization

Simulation & Research Data

  • Simulation data with error bounds
  • Native uncertainty representation
  • Visual encoding of confidence
  • Better scientific communication

Fields: Climate modeling, particle physics, computational fluid dynamics

⚙

AI-Driven Enhancement

Learned Probabilistic Processing

  • Neural networks predict optimal variance
  • Learned correction channels
  • Adaptive encoding via reinforcement learning
  • Continuous optimization

Future: Direct training of stochastic operations on rate-distortion objectives

▲

Real-Time Ray Tracing

AR/VR Graphics Acceleration

  • Stochastic ray tracing for AR/VR
  • Leverage inherent randomness
  • Monte Carlo rendering acceleration
  • Power-efficient immersive experiences

Enables: High-quality real-time rendering on mobile VR headsets

Key Benefits Summary

Comprehensive advantages of the hybrid SDPA architecture across performance, efficiency, and deployment

⚡

Energy Efficiency

  • 60-70% power reduction vs full deterministic
  • <100µW per entropy source
  • Stochastic processing uses 85% of pixels
  • Extended battery life for mobile devices
▶

Performance

  • 2-5× latency improvement
  • Parallel processing of 64-256 pixels simultaneously
  • Real-time adaptive quality control
  • Perceptually equivalent visual quality
◆

Smart Allocation

  • Precision only where needed (15% of pixels)
  • Dual-source confidence validation
  • Runtime convergence monitoring
  • Self-correcting architecture
↻

Adaptability

  • Dynamic quality-performance tradeoffs
  • Power/thermal constraint awareness
  • Content-adaptive processing
  • Feedback-driven optimization
■

Deployment Flexibility

  • Works on existing devices (software)
  • Progressive enhancement model
  • Three-tier adoption strategy
  • Backward compatibility via fallback
◎

Extensibility

  • Beyond video: audio, imaging, data
  • AI-driven enhancements
  • Scientific and medical applications
  • Future-proof architecture

The Future of Efficient Media Processing

By combining the efficiency of Stochastic Computing with the precision of GPU processing, guided by confidence metadata and enabled by the Probabilistic Media Format, SDPA represents a fundamental advancement in energy-efficient, high-quality media systems for mobile, IoT, automotive, and beyond.