DL Chip Guide: GPU, TPU to FPGA

🔑 Enhanced Key Takeaways

• •The emergence of Processing-in-Memory (PIM) and Near-Memory Computing (NMC) architectures is specifically targeting the 'memory wall' by integrating logic directly into DRAM or HBM stacks to minimize data movement energy costs.
• •Interconnect technology, such as NVLink and CXL (Compute Express Link), has become as critical as the compute silicon itself, enabling disaggregated memory pools that allow multiple GPUs to share a unified KV cache for massive Transformer models.
• •The industry is shifting toward 'domain-specific' software-defined hardware, where compilers like MLIR (Multi-Level Intermediate Representation) are increasingly responsible for mapping high-level graph operations to hardware-specific primitives, effectively abstracting the underlying silicon complexity.

🛠️ Technical Deep Dive

• •Systolic Arrays: Utilize a grid of Processing Elements (PEs) where data flows through the array in a rhythmic, pipelined fashion, minimizing register file access by reusing data across adjacent PEs.
• •KV Cache Optimization: Techniques like PagedAttention (vLLM) and Multi-Query Attention (MQA) are being implemented at the hardware-compiler interface to reduce memory fragmentation and bandwidth pressure during autoregressive decoding.
• •Mixed-Precision Arithmetic: Modern accelerators leverage FP8 (E4M3/E5M2 formats) to double throughput compared to BF16 while maintaining sufficient numerical stability for inference, often supported by hardware-level stochastic rounding.
• •FPGA Spatial Pipelines: Unlike GPUs that rely on SIMT (Single Instruction, Multiple Threads), FPGAs implement custom data-flow architectures where the hardware circuit is reconfigured to match the specific layer topology of a CNN or Transformer, achieving deterministic latency.

🔮 Future ImplicationsAI analysis grounded in cited sources