Understanding the Memory Bandwidth Bottleneck in LLMs
Autoregressive token generation is fundamentally memory-bound rather than compute-bound. During each generation step, every parameter in the model must be loaded from memory into processor registers to compute a single token. Even with advanced GPUs, memory bandwidth limitations cap token generation speeds. InferAccel focuses heavily on maximizing HBM3e throughput efficiency.
Advanced Bank Interleaving and Access Scheduling
InferAccel implements intelligent memory controller scheduling that optimizes bank interleaving and column-address strobe timing across HBM3e stacks. By preventing memory bank conflicts and reducing DRAM refresh cycle latency, InferAccel increases effective memory bus utilization by up to 28%.
Multi-Level On-Chip SRAM Caching and Activation Reuse
To minimize costly trips to off-chip HBM, InferAccel structures multi-level on-chip SRAM hierarchies to cache frequently accessed rotary embedding tables, normalization weights, and prompt prefix KV-cache blocks directly adjacent to arithmetic logic units.
Reducing DRAM Energy Consumption via Data Compression
Active memory transfers consume the vast majority of an accelerator's power budget. InferAccel incorporates lossless on-the-fly tensor compression circuits within memory controllers, compressing intermediate activation tensors before writing to DRAM and reducing overall memory subsystem energy consumption.
