Technology module
SRAM-CIM compute path
The efficiency limit of AI inference is rarely the math itself — it is moving data back and forth. VXI builds on SRAM compute-in-memory so weights stay where computation happens, leaving energy and bandwidth for the work that matters.
Architecture · Dataflow · Efficiency
Why it matters
Weight residency and near-memory compute are the architecture direction for reducing unnecessary data movement.
Data-centric compute
Weights reside next to the compute arrays, sharply reducing trips to external memory for a shorter, steadier inference path.
Built for inference workloads
Dataflow is planned around multiply-accumulate and attention patterns, so model behavior maps directly onto the hardware path.
Efficiency and experience together
Less data movement means lower system power pressure and more consistent responsiveness for always-on edge products.
How it works
Model analysis
Profile model structure and compute hot spots to decide which weights and layers should reside in the compute arrays.
Weight mapping
Place weights onto SRAM-CIM arrays according to memory capacity and dataflow needs.
In-array compute
Complete multiply-accumulate where the data lives and pass on only the intermediate results that matter.
Result aggregation
The data fabric and runtime gather outputs and hand them to the next stage of the model and application.
What we align on during evaluation
Bring this information to your evaluation request so the VXI team can propose a concrete platform and adoption plan faster.
- Target model architecture, precision needs, and dominant operator types
- Product priorities across latency, throughput, and power
- Expected weight footprint and how much can stay resident
- Work split with the host processor and external memory
Frequently asked questions
- What is SRAM-CIM?
- SRAM-CIM (SRAM compute-in-memory) is an architecture approach that performs computation inside or right next to memory arrays. Data no longer shuttles between memory and compute units, which is a key path to more efficient AI inference.
- Which AI applications benefit most?
- Inference workloads dominated by matrix operations benefit the most, such as language, vision, and multimodal applications. VXI maps your model and product constraints to the best-fit platform and data placement.
- Do existing models need retraining?
- In most cases, no. The VXI toolchain supports model conversion and quantization, and the evaluation phase confirms accuracy and performance targets with you.
Related technology modules
- Memory hierarchy — Co-placed weights, activations, and cache
- On-chip data fabric — Workload-aware data paths and module communication
- Compiler and runtime — From model conversion and quantization to runtime control