What are the known limitations of libaom on low-power ARM devices?

Libaom, the open-source reference encoder and decoder implementation for the AV1 video format, faces major performance hurdles when deployed on low-power ARM architectures, such as those found in IoT devices, older smartphones, and entry-level single-board computers. Because libaom was originally designed as a research-first reference tool, it heavily emphasizes compression efficiency over processing speed. On resource-constrained ARM hardware, this algorithmic complexity translates directly into high CPU utilization, severe frame-rate drops, and excessive battery drain.

Extreme Computational Complexity

The primary bottleneck of libaom is the sheer mathematical complexity of the AV1 specification. AV1 utilizes highly sophisticated coding tools, including exhaustive block partitioning (ranging from \(128 \times 128\) down to \(4 \times 4\) pixels), intra-block copy, warped motion compensation, and advanced in-loop filtering like the Constrained Directional Enhancement Filter (CDEF). On low-power ARM cores, which lack the massive clock speeds and deep instruction pipelines of desktop processors, evaluating these multi-layered coding options causes encoding speeds to crawl, often making real-time software encoding mathematically impossible.

Incomplete or Suboptimal SIMD Vectorization

While libaom has received significant updates to support ARM NEON (and newer SVE/SVE2) assembly optimizations, its vectorization is historically less mature than its x86 AVX2 or AVX-512 counterparts. Many specialized AV1 tools inside the libaom codebase still lack fully optimized NEON implementation paths. When the encoder encounters these unoptimized code paths, it falls back to standard C routines, stripping the low-power ARM chip of its hardware-accelerated vector processing advantages and capping performance.

High Memory Bandwidth and Cache Constraints

Libaom utilizes multi-frame lookahead buffers, comprehensive reference frame tracking, and large spatial-temporal filtering graphs to maximize data compression. Low-power ARM Systems-on-Chip (SoCs) typically feature highly constrained L1/L2/L3 cache sizes and limited memory bandwidth via low-voltage LPDDR RAM. The heavy memory footprint of libaom causes frequent cache misses and forces the CPU to constantly fetch data from the slower main memory, creating a severe hardware bottleneck.

Thermal Throttling and Power Inefficiency

Low-power ARM devices are usually passively cooled and designed to operate within strict thermal and power limits. Because libaom maximizes CPU usage across all available threads to process its algorithms, running an encode or decode session quickly drives the SoC to its thermal threshold. To prevent damage, the device responds by aggressively throttling its CPU frequency. This creates a compounding performance drop where the encoder runs slower and slower the longer it operates.

In-Loop Filtering Bottlenecks during Decoding

Even when used strictly for decoding, libaom encounters structural limitations on weak ARM hardware. The sequential nature of AV1’s loop restoration and CDEF processing makes parallelism difficult to achieve at the macroblock level. Without dedicated fixed-function hardware decoders built into the ARM SoC, software-based decoding via libaom strains the processor, leading to dropped frames during high-bitrate 1080p or 4K playback and rapid battery depletion.