Compute
is the new
infrastructure
of civilization
Our future depends
on compute
TALPs apply everywhere compute runs
TALPs improve compute throughput and predictability — slashing power consumption while accelerating the speed of execution.
In lab tests, TALPs have reduced energy consumption by as much as 91%.
Matrix Multiply
TALPs Optimize Software for the Environment the Software Runs In
Software doesn't run in a vacuum - it runs on real hardware with real-world constraints. TALPs dynamically optimize execution for the specific environment in which software operates, improving performance, reducing energy consumption, or achieving the optimal balance between the two.
Everyone's Focused on
Making Hardware Faster.
Few Have Focused on
Optimizing How Software
Actually Runs.
For decades, the industry has focused on: Smaller transistors, Higher clock speeds, More cores, Faster interconnects
But software execution itself — the way processors step through machine code — has remained fundamentally unmanaged.
Processors execute instructions.
They do not understand execution pathways.
TALPs do.
Compute has expanded. Optimization has not.
Five fundamentals principals that make TALPs a foundation for modern compute.
Ubiquitous by Design
TALPs apply everywhere compute runs.
From Cloud to Edge. From Watts to Milliwatts.
Compute is expanding across every layer of modern systems. Cloud infrastructure, industrial equipment, field systems, and personal devices all rely on software execution.
- Hyperscale + enterprise workloads
- Industrial plants + real-time systems
- Field equipment + rugged edge compute
- Personal devices + local execution
Cross-Industry Impact
Any domain. Same advantage.
Every vertical is becoming computational.
As software becomes the control plane for the physical world, performance and efficiency become strategic—across mission-critical and specialized systems.
- Defense + national security systems
- Enterprise + infrastructure software
- Biotech + genomics + scientific computing
- AI + IoT + robotics + edge networks
No New Hardware
No silicon redesign required.
Optimize software. Not silicon.
TALPs improve execution on your target architecture—without a chip redesign, fabrication cycle, manufacturing ramp, or ecosystem migration.
- No new hardware development cycle
- No new manufacturing or supply chain risk
- No platform fragmentation for users
- Ship improvements as software updates
Beyond Parallelization
More than parallelism.
Whole-program optimization: serial + parallel.
TALPs don’t just “add threads.” They optimize the serial path and extract safe parallel execution where it exists—improving throughput and predictability.
- Optimize serial bottlenecks
- Extract safe parallel execution pathways
- Control synchronization only where necessary
- Improve predictability and utilization
Automatic TALPification
Adoption without retraining.
No parallel programming required.
Teams shouldn’t have to rewrite systems around new models or train on niche frameworks. TALPification is automatic—software stays software.
- No kernel rewrites or framework lock-in
- No training teams on new parallel models
- Works with existing code structure
- Clear, auditable transformations
Different layers of optimization
Compiler and TALPdistinct roles, stronger together
A compiler optimizes how code is translated and emitted for a target machine. A TALP optimizes which execution pathway matters, how that pathway behaves with real input, and how it should run to meet a real goal such as speed, energy, memory, or cost.
Compiler lane
How can this code be emitted efficiently for the machine?
What it does
- Syntax and semantic analysis
- IR transforms and code generation
- Inlining, vectorization, scheduling
- Register allocation and binary emission
Optimizes
Optimizes code translation and machine-facing efficiency
TALP lane
Which pathway will execute, how will it behave, and what is the best way to run it?
What it does
- Execution pathway decomposition
- Input-sensitive loop and behavior analysis
- Prediction of time, energy, memory, and speedup
- Goal-driven execution and parallelization strategy
Optimizes
Optimizes pathway behavior and workload-specific execution
How they work together
TALP decides what to optimize.The compiler decides how to emit it efficiently.
TALP makes the execution pathway explicit, predicts runtime behavior for real workloads, and chooses a strategy aligned to real goals. The compiler then turns that strategy into efficient executable code for the target machine.
TALP determines what pathway matters and how it should run
The compiler emits efficient executable code for that strategy
Together they improve both runtime behavior and machine-level efficiency
TALP + Compiler relationship
A stacked view of optimization
TALP operates above the compiler layer, reasoning about application behavior and execution pathways. The compiler operates below that, translating code efficiently for the processor. Together they form a practical optimization stack.
Models how software actually behaves and which execution pathway matters.
Transforms program code into efficient instructions for the target machine.
Target execution layer
Processor / Machine
The compiler emits for the machine. TALP shapes how the application should execute on that machine.
Application-aware decomposition, pathway selection, and execution planning.
IR generation, code transformation, and machine-targeted emission.
Better execution strategy above, efficient code generation below.
Two layers of optimization
Decision and Execution
TALP operates as an execution intelligence layer above the compiler. It determines how a program should run. The compiler then translates that decision into efficient machine-level execution.
TALP
Execution Intelligence Layer
Decides what will run, how it behaves, and what matters for this execution.
COMPILER
Execution Translation Layer
Turns code into efficient instructions for the target machine.
Target execution
Processor / Machine
TALP decides how your program should run.
The compiler makes that decision executable.
TALP in the software lifecycle
TALP fits across the lifecycle
TALP is not a competing replacement for compilers, profilers, analytics, or deployment tooling. It acts as an execution intelligence layer that works across the lifecycle and makes the existing stack smarter.
TALP
Execution Intelligence Layer
Understands software, hardware, data, and goals — then informs optimization decisions across the lifecycle.
Source, algorithms, architecture
Toolchains, packaging, integration
IR, codegen, machine targeting
Validation, tracing, performance data
Targets, environments, rollout paths
Execution, control, optimization
What this means
TALP works with the lifecycle — not against it
Instead of replacing existing tools, TALP adds a higher-order layer of execution intelligence that can inform code analysis, optimization choices, compilation strategy, deployment context, and runtime behavior.
Works with compilers
TALP informs execution strategy while compilers still handle code translation and machine-level emission.
Works with profiling and analytics
TALP complements observability, tracing, and performance analysis by turning execution behavior into actionable optimization decisions.
Works with optimization tooling
TALP does not replace existing optimization tools. It adds a pathway-aware intelligence layer above them.
Works at runtime
TALP helps determine how software should execute for real hardware, real inputs, and real goals.
Serial code vs TALPified execution.
The “after” version is not about rewriting everything—it’s about declaring the pathway and constraints. The runtime finds safe parallelism automatically.
▍▍Pseudo-code for clarity. Actual integration details depend on workload and environment.
When Compute
Becomes Self-Aware…
Software no longer blindly executes instructions. It observes the pathways it actually runs, understands how input data changes execution, and adapts in real time to achieve the best possible outcome.
Observe
Sees the execution pathways your software actually takes.
Understand
Learns how data, hardware, and runtime conditions shape behavior.
Adapt
Adjusts execution dynamically to pursue the best outcome in real time.
Execution State
Observing pathways. Adapting in real time.
Self-Aware Compute
When Compute Becomes Self-Aware…
Software no longer blindly executes instructions. It observes the pathways it actually runs, understands how input data changes execution, and adapts in real time to achieve the best possible outcome.
Observe
Sees the execution pathways your software actually takes.
Understand
Learns how data shape, hardware, and runtime conditions affect behavior.
Adapt
Adjusts execution dynamically to pursue the best outcome in real time.
Move the goals. Watch the system adapt.
Adjust input size, core availability, timing targets, energy goals, and cost assumptions. Then compare optimized output against the serial, unoptimized baseline.