AV1 vs VP9 Decoder Memory Cache Efficiency

This article examines the distinct differences in memory cache efficiency between AV1 and VP9 decoders, detailing how their architectural features impact hardware caching subsystems. While AV1 delivers significantly higher compression efficiency than VP9, its advanced toolset—including multi-symbol entropy coding, extended reference frames, warped motion compensation, and multi-stage in-loop filtering—introduces irregular memory access patterns, larger working sets, and increased pressure on L1, L2, and L3 caches compared to the simpler pipeline of VP9.

Reference Frame Buffering and Inter-Prediction

VP9 maintains a pool of eight reference frames in memory but restricts inter-prediction to selecting from only three frames per predicted inter-picture: Last, Golden, and AltRef. Because fewer reference pictures are actively sampled simultaneously, VP9 decoders exhibit strong temporal and spatial locality, allowing reference block data to fit comfortably within modern L2 and L3 caches.

AV1 expands this capability, allowing a single frame to reference up to seven active reference pictures simultaneously from its buffer. Querying up to seven distinct reference planes during block reconstruction scatters read requests across wider memory boundaries. This higher working-set footprint substantially increases the likelihood of L2 cache evictions, forcing the decoder to fall back to higher-latency L3 cache reads or system DRAM.

Motion Compensation and Access Patterns

VP9 relies on standard translational motion vectors with sub-pixel interpolation filters. Its memory access patterns during motion compensation are regular and predictable, which aligns well with hardware prefetchers that pre-load consecutive cache lines.

AV1 introduces Warped Motion Compensation (affine transformation) and Global Motion. Warped motion creates non-axis-aligned, arbitrary affine sampling grids. When calculating sub-pixel interpolation across these sheared or rotated areas, the decoder must fetch non-contiguous memory locations across multiple cache lines to decode a single block. This non-linear access pattern impairs hardware prefetchers, leading to elevated L1 data (L1D) cache miss rates and pipeline stalls.

Superblocks and Partitioning Granularity

VP9 utilizes a quadtree partition structure with a maximum superblock size of 64x64 down to 4x4. The relatively uniform quadtree layout facilitates predictable, sequential block processing across memory rows.

AV1 raises the maximum superblock size to 128x128 and incorporates complex, non-quadtree partition types, including 1:4 and 4:1 rectangular splits. Processing a 128x128 superblock demands larger internal buffers for intermediate prediction and transform coefficients. If an implementation processes an entire 128x128 block pipeline in-place, the intermediate state can exceed the capacity of fast L1D caches, requiring frequent spills into the slower L2 cache.

In-Loop Filtering and Line Buffer Footprint

The in-loop filtering pipeline is one of the most cache-sensitive stages in video decoding:

• VP9 Loop Filter: VP9 employs a single-stage, edge-directed deblocking filter. Because only adjacent boundary pixels are modified, the vertical and horizontal line-buffer requirements remain compact. The filter operations demonstrate high spatial locality and are easily kept within L1 and L2 caches.
• AV1 Multi-Stage Filtering: AV1 chains three distinct filters: the standard Deblocking Filter, the Constrained Directional Enhancement Filter (CDEF), and the Loop Restoration filter (Wiener or Self-Guided). CDEF requires analyzing 8x8 blocks along directional edges, and Loop Restoration applies large-kernel filtering (up to 7x7). Stacking these filters requires the decoder to store several rows of pixel data (line buffers) across multiple pipeline stages. If the cumulative line-buffer size exceeds the capacity of the local L1 or L2 cache, intermediate pixel rows must be repeatedly flushed to and retrieved from external memory, degrading overall memory bandwidth efficiency.

Entropy Decoding and Context Modeling

VP9 utilizes a binary arithmetic coding engine with static and dynamically adapted 8-bit probabilities. The context models are small, allowing the entire probability state to remain permanently resident in L1D cache during frame parsing.

AV1 uses a multi-symbol arithmetic coder operating on Cumulative Distribution Functions (CDFs) with 15-bit precision. AV1 tracks a vastly larger number of syntax contexts, and its CDF tables occupy significantly more memory. Updating and reading these larger CDF arrays during bitstream parsing increases L1D cache footprint, occasionally evicting other critical decoding parameters from the cache during bitstream ingest.