Static Memory Planning

By analyzing the entire computational graph at compile time, the compiler calculates the peak memory usage and allocates a single, contiguous memory arena. This eliminates the risk of runtime memory fragmentation and out-of-memory errors, which are critical failures in embedded systems. The footprint is fixed and known before deployment, enabling reliable execution on devices with strict memory budgets (e.g., 512KB SRAM).

Static planning removes all calls to dynamic memory allocators (like malloc or new) during inference. This provides major performance gains:

• Eliminates allocator latency: No time spent searching for free memory blocks.
• Reduces CPU cache pollution: Predictable memory access patterns improve cache efficiency.
• Ensures real-time guarantees: Essential for control systems and sensor processing where jitter is unacceptable. The memory is simply assigned from the pre-allocated pool via pointer arithmetic.

The compiler performs liveness analysis on every tensor to identify non-overlapping lifetimes. It then assigns the same physical memory buffer to multiple tensors, a process called in-place operation or memory sharing. For example, the output buffer of a convolutional layer can be reused for the subsequent ReLU activation, and then again for a pooling layer, dramatically reducing the total memory required.

A static memory map is a prerequisite for other critical edge compiler techniques:

• Operator Fusion: Fused kernels can share intermediate buffers without spilling to main memory.
• Memory Tiling: Tile sizes for loops can be optimized based on known, fixed buffer sizes to maximize cache hits.
• Direct Memory Access (DMA) Scheduling: Data movement between memory hierarchies can be pre-planned and overlapped with computation.

Dynamic memory allocation, common in frameworks like PyTorch eager mode, allocates and frees memory per operator at runtime. This creates non-deterministic latency and memory fragmentation. Static planning, as used in Ahead-Of-Time (AOT) compilers like TVM or TFLite, shifts this cost to compile time. The trade-off is loss of flexibility for dynamic tensor shapes, which must be handled via techniques like shape inference or multiple pre-compiled plans.

Static plans are tightly coupled to the target hardware's memory hierarchy. The compiler's Hardware Abstraction Layer (HAL) uses knowledge of fast scratchpad SRAM vs. slower DRAM to place buffers strategically. For Neural Processing Units (NPUs), it maps tensors to specific accelerator-internal memory banks. This co-design between the static plan and hardware resources is key to achieving peak performance on edge silicon.

A high-level compiler pass that transforms a neural network's computational graph to improve execution efficiency. This is the foundational stage where optimizations like operator fusion and constant folding are applied, creating a streamlined graph that is then passed to lower-level optimizations like static memory planning. These transformations reduce computational overhead and prepare the model for hardware-specific code generation.

A compilation strategy where the entire model is optimized and translated into an executable binary before runtime. This is the primary context for static memory planning, as it allows the compiler to perform a complete analysis of the entire inference graph. The benefits include:

• Deterministic memory footprint: All buffers are pre-allocated.
• Minimal startup latency: No runtime compilation or allocation overhead.
• Enhanced security: The fixed binary is easier to verify and harden.

A compiler optimization that partitions large tensors into smaller blocks (tiles) to fit into fast, on-chip memory (e.g., SRAM, cache). While static memory planning allocates buffers, tiling optimizes how data is moved and computed within those buffers. It is crucial for performance on edge accelerators (NPUs) with hierarchical memory, as it:

• Maximizes data locality.
• Reduces costly accesses to slower main memory (DRAM).
• Enables efficient pipelining of compute and data transfer.

A low-level optimization that merges multiple primitive operations (e.g., Conv2D, BatchNorm, ReLU) into a single, custom compute kernel. This directly complements static memory planning by reducing the number of intermediate tensors that need to be allocated and written to memory. Benefits include:

• Elimination of intermediate buffer writes/reads.
• Reduced kernel launch overhead.
• Improved utilization of register files and cache.

A software layer within the compiler stack that provides a standardized interface for generating code and managing resources across diverse hardware accelerators. The static memory planner interacts with the HAL to understand the specific memory hierarchy (e.g., SRAM sizes, DMA capabilities) of the target NPU or MCU. This allows it to make optimal allocation decisions, such as placing frequently accessed tensors in fast scratchpad memory managed by the HAL.

A compiler infrastructure that supports multiple, interoperable levels of IR abstraction. Static memory planning is typically implemented as a compiler pass within an MLIR-based stack. It operates on a mid-level IR representing the computational graph and tensor buffers, transforming it into a lower-level IR where memory addresses are assigned. MLIR's modularity allows this optimization to be reused across different frontend frameworks and hardware backends.