Algorithm Discovery
Neural Architecture Search
Generate novel neural network architectures with target accuracy, latency, and parameter budgets through constraint-based construction beyond established architecture families.
The Challenge
Why Neural Architecture Search needs a new approach to generation
The neural architecture search space is enormous — layer types, connection patterns, channel widths, attention configurations, and normalization choices create a combinatorial explosion that current NAS methods explore through expensive proxy tasks or restricted search spaces. Most NAS approaches evaluate thousands of architectures to find good candidates, requiring computational budgets that limit exploration to narrow families of known building blocks.
Weight-sharing supernets and one-shot NAS methods reduce search cost but constrain exploration to predefined architectural primitives, struggling with hardware-aware multi-objective optimization where accuracy, latency, and memory must be simultaneously satisfied. Evolutionary and reinforcement learning approaches explore more broadly but require thousands of GPU-hours and produce architectures anchored to the operator sets defined by researchers.
The MatterSpace Approach
How MatterSpace generates for neural architecture search
MatterSpace Algo generates neural architectures through constraint-based construction — specify accuracy targets, latency budgets, memory limits, and target hardware, and Algo constructs complete architectures satisfying all constraints simultaneously. The generation process explores connectivity patterns and operator combinations beyond predefined search spaces, producing architectures that are novel by construction.
The NAS domain pack encodes architecture-performance relationships, hardware cost models for major deployment targets, and training efficiency predictors. Users define deployment constraints and Algo generates architectures with predicted performance profiles on target hardware, accompanied by training recipes optimized for the generated topology.
Constraint-Based Generation
Specify what the output must satisfy. MatterSpace constructs candidates that meet all constraints simultaneously.
Valid by Construction
Every output satisfies physical laws, stability criteria, and domain constraints — no post-hoc filtering needed.
MatterSpace Algo
Powered by a domain-specific generation engine with physics-aware priors and adaptive dynamics control.
Generation Output
What MatterSpace generates
- Novel network architectures with predicted accuracy-latency profiles
- Hardware-optimized topology designs for specific deployment targets
- Training recipes for generated architectures
- Multi-objective Pareto-optimal architecture sets across competing constraints
Key Differentiators
Why MatterSpace is different
MatterSpace Algo generates architectures outside established families (ResNet, Transformer variants), discovering novel connectivity patterns and operator combinations that predefined search spaces exclude. Hardware constraints are enforced during generation rather than filtered post-hoc, producing deployment-ready architectures with predicted accuracy-latency-memory trade-off profiles.
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Start generating with MatterSpace
Whether you are exploring neural architecture search for the first time or scaling an existing research programme, MatterSpace generates novel candidates that satisfy your constraints by construction.
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