Efficiency from Sparsity
By pruning neural networks to only store and process the necessary components, we reduce power consumption by 100x and memory footprint by 10x — all with minimal effect on performance.
Sparsity in Action
femtoAI’s sparsity-first design consumes significantly less power by activating only the neurons that matter.
SPU Architecture
Sparsity Acceleration
- Sparse-aware hardware can compute math 10x faster
- Sparse-data can compress memory 10x to maximize density
- Sparse-algorithms can save 100x more power with optimized instruction-set
Near-Memory Compute
- Increase power efficiency by reducing data movement
- Eliminate energy and throughput bottlenecks by retaining workloads on-chip
- Deliver full accuracy and model flexibility beyond in-memory compute
Scalable Core
- Match needs of your specific deployment with scalable architectures
- Reuse core IP across a range of applications with scalable solutions
- Boost throughput and save power by distributing workloads
Sparsity-Enhanced Tooling
Build with Sparsity
- Design new neural networks that maximize available memory, power, and bandwidth.
- Optimize existing neural networks for speed, efficiency, and memory footprint with minimal impact on performance.
Frontend to Binary
- Prune, quantize, and sparsify models from standard ML frameworks using the femtoAI Model Optimization Toolkit.
- Compile models into highly efficient SPU binaries that fully exploit sparsity, memory layout, and hardware instructions.
AI Factory
- Train and fine-tune models using proven, sparsity-aware recipes optimized for edge deployment.
- Low-code workflows enable rapid iteration across accuracy, power, and latency from prototype to production.
