typo: update README.md: fixing a typo for "hierical" (#836)

"hierical" -> "hierarchical"

Author: didier-durand

README.md

@@ -23,7 +23,7 @@ Check our [v0.2 release blog](https://flashinfer.ai/2024/12/16/flashinfer-v02-re
 The core features of FlashInfer include:
 1. **Efficient Sparse/Dense Attention Kernels**: Efficient single/batch attention for sparse(paged)/dense KV-storage on CUDA Cores and Tensor Cores (both FA2 & FA3) templates. The vector-sparse attention can achieve 90% of the bandwidth of dense kernels with same problem size.
 2. **Load-Balanced Scheduling**: FlashInfer decouples `plan`/`run` stage of attention computation where we schedule the computation of variable-length inputs in `plan` stage to alleviate load-imbalance issue.
-3. **Memory Efficiency**: FlashInfer offers [Cascade Attention](https://docs.flashinfer.ai/api/cascade.html#flashinfer.cascade.MultiLevelCascadeAttentionWrapper) for hierical KV-Cache, and implements Head-Query fusion for accelerating Grouped-Query Attention, and efficient kernels for low-precision attention and fused-RoPE attention for compressed KV-Cache.
+3. **Memory Efficiency**: FlashInfer offers [Cascade Attention](https://docs.flashinfer.ai/api/cascade.html#flashinfer.cascade.MultiLevelCascadeAttentionWrapper) for hierarchical KV-Cache, and implements Head-Query fusion for accelerating Grouped-Query Attention, and efficient kernels for low-precision attention and fused-RoPE attention for compressed KV-Cache.
 4. **Customizable Attention**: Bring your own attention variants through JIT-compilation.
 5. **CUDAGraph and torch.compile Compatibility**: FlashInfer kernels can be captured by CUDAGraphs and torch.compile for low-latency inference.
 6. **Efficient LLM-specific Operators**: High-Performance [fused kernel for Top-P, Top-K/Min-P sampling](https://docs.flashinfer.ai/api/sampling.html) without the need to sorting.