Files
kernbench2/topology.yaml
T
ywkang b6eb97c49a Implement ADR-0021: PE pipeline refactor with token self-routing
Step 1-2: Backup existing code
- builtin/ → builtin_legacy/ (unchanged backup)
- custom/pe_accel/ → custom/pe_accel_legacy/ (unchanged backup)

Step 3-4: New pipeline types and tiling
- pe_types.py: StageType, Stage, TilePlan, PipelinePlan, PipelineContext, TileToken
- tiling.py: generate_gemm_plan, generate_math_plan (ported from pe_accel)

Step 5: Component implementations (ADR-0021 D4-D6)
- PE_SCHEDULER: _feed_loop (singleton FIFO feeder) + plan generation
- PE_FETCH_STORE: new component — TCM ↔ Register File
- PE_GEMM: TileToken pipeline + legacy PeInternalTxn dual-mode
- PE_MATH: TileToken pipeline + legacy dual-mode
- PE_DMA: TileToken pipeline + legacy + fabric Transaction triple-mode
- PE_TCM: TcmRequest handler with dual-channel BW serialization

Step 6: Infrastructure
- topology.yaml: pe_fetch_store component + chaining edges
- components.yaml: pe_fetch_store_v1 registration
- builder.py: PE_COMP_OFFSETS, _add_pe_internal_edges, PE view positions
- Tests: node/edge counts, PE component sets updated

All components handle both TileToken (pipeline) and PeInternalTxn (legacy).
Token self-routing: components read next stage from token.plan, chain via out_port.
366 tests passing.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
2026-04-08 23:35:31 -07:00

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system:
ns_per_mm: 0.01 # wire propagation delay: 10 ps/mm (on-chip silicon)
sips:
count: 2
components:
switch: { kind: switch, impl: switch_v1, attrs: { overhead_ns: 5.0 } }
links:
io_ep_to_switch:
kind: pcie
bw_gbs_per_ep: 768.0
distance_mm: 20.0
sip:
cube_mesh: { w: 4, h: 4 }
iochiplet:
components:
pcie_ep: { kind: pcie_ep, impl: pcie_ep_v1, attrs: { overhead_ns: 5.0 } }
io_cpu: { kind: io_cpu, impl: io_cpu_v1, attrs: { overhead_ns: 10.0 } }
io_noc: { kind: io_noc, impl: forwarding_v1, attrs: { overhead_ns: 0.0 } }
links:
pcie_ep_to_noc_bw_gbs: 256.0
pcie_ep_to_noc_mm: 1.0
io_cpu_to_noc_bw_gbs: 256.0
io_cpu_to_noc_mm: 0.5
ucie:
overhead_ns: 8.0
n_connections: 4
per_connection_bw_gbs: 128.0 # 4 × 128 = 512 GB/s = PHY BW
noc_to_ucie_mm: 0.5
instances:
- id: io0
place: { side: N, offset_norm: 0.5 }
ucie: { phy_bw_gbs: 512.0, phys: [P0, P1, P2, P3] }
cube_ports:
- { cube: {xy: [0,0]}, cube_side: N, phy: P0, distance_mm: 2.0 }
- { cube: {xy: [1,0]}, cube_side: N, phy: P1, distance_mm: 2.0 }
- { cube: {xy: [2,0]}, cube_side: N, phy: P2, distance_mm: 2.0 }
- { cube: {xy: [3,0]}, cube_side: N, phy: P3, distance_mm: 2.0 }
links:
inter_cube_mesh:
bw_gbs_per_ucie_phy: 512.0
distance_mm_across_seam: 1.0
routing: { algo: xy }
cube:
geometry:
cube_mm: { w: 17.0, h: 14.0 }
hbm_mm: { w: 9.0, h: 5.0 }
ucie_mm: { size: 2.0 }
pe_layout:
corners: [NW, NE, SW, SE] # N corners → top PE rows; S corners → bottom PE rows
pe_per_corner: 2 # total PEs per cube: 4 * 2 = 8
pe_template:
components:
pe_cpu: { kind: pe_cpu, impl: pe_cpu_v1, attrs: { overhead_ns: 2.0 } }
pe_scheduler: { kind: pe_scheduler, impl: pe_scheduler_v2, attrs: { overhead_ns: 1.0 } }
pe_dma: { kind: pe_dma, impl: pe_dma_v1, attrs: { rd_engines: 1, wr_engines: 1 } }
pe_gemm: { kind: pe_gemm, impl: pe_gemm_v1, attrs: { overhead_ns: 0.0, shared_resource: accel_slot, peak_tflops_f16: 8.0 } }
pe_math: { kind: pe_math, impl: pe_math_v1, attrs: { overhead_ns: 0.0, shared_resource: accel_slot } }
pe_fetch_store: { kind: pe_fetch_store, impl: pe_fetch_store_v1, attrs: { overhead_ns: 0.0 } }
pe_mmu: { kind: pe_mmu, impl: pe_mmu_v1, attrs: { tlb_overhead_ns: 0.5, page_size: 4096 } }
pe_tcm: { kind: pe_tcm, impl: pe_tcm_v1, attrs: { size_mb: 16, read_bw_gbs: 512.0, write_bw_gbs: 512.0 } }
links:
pe_cpu_to_scheduler_mm: 0.5
scheduler_to_dma_mm: 0.5
scheduler_to_gemm_mm: 0.5
scheduler_to_math_mm: 0.5
scheduler_to_fetch_store_mm: 0.5
dma_to_tcm_bw_gbs: 512.0
dma_to_tcm_mm: 0.5
dma_to_fetch_store_mm: 0.0 # DMA → fetch_store chaining (ADR-0021)
fetch_store_to_tcm_bw_gbs: 512.0
fetch_store_to_tcm_mm: 0.0
fetch_store_to_gemm_mm: 0.0 # fetch → GEMM chaining (ADR-0021)
fetch_store_to_math_mm: 0.0 # fetch → MATH chaining (ADR-0021)
gemm_to_fetch_store_mm: 0.0 # GEMM → store chaining (ADR-0021)
math_to_fetch_store_mm: 0.0 # MATH → store chaining (ADR-0021)
fetch_store_to_dma_mm: 0.0 # store → DMA writeback chaining (ADR-0021)
gemm_to_tcm_bw_gbs: 512.0
gemm_to_tcm_mm: 0.5
math_to_tcm_bw_gbs: 512.0
math_to_tcm_mm: 0.5
memory_map:
hbm_total_gb_per_cube: 48
hbm_slices_per_cube: 8
hbm_total_bw_gbs: 1024.0
hbm_mapping_mode: n_to_one # one_to_one | n_to_one (ADR-0019)
hbm_pseudo_channels: 64 # total pseudo channels per cube
hbm_channels_per_pe: 8 # = pseudo_channels / pes_per_cube
hbm_channel_bw_gbs: 32.0 # per-channel bandwidth (GB/s)
components:
noc_router: { kind: noc_router, impl: forwarding_v1, attrs: { overhead_ns: 2.0 } }
m_cpu: { kind: m_cpu, impl: m_cpu_v1, attrs: { overhead_ns: 5.0 } }
hbm_ctrl: { kind: hbm_ctrl, impl: hbm_ctrl_v1, attrs: { capacity: 1, efficiency: 1.0 } }
sram: { kind: sram, impl: sram_v1, attrs: { size_mb: 32, overhead_ns: 2.0 } }
# Physical placement of non-PE components (mm coordinates)
placement:
m_cpu: { pos_mm: [7.5, 3.0] } # top center, below UCIe-N
sram: { pos_mm: [1.5, 9.0] } # left side, below HBM zone
ucie:
decompose: true
ports: [N, S, E, W]
overhead_ns: 8.0
n_connections: 4 # independent NOC↔UCIe connections per port
per_connection_bw_gbs: 128.0 # BW per connection; 4 × 128 = 512 GB/s = UCIe PHY BW
links:
# Router mesh links (ADR-0019)
router_link_bw_gbs: 256.0 # inter-router XY mesh link BW
router_overhead_ns: 2.0 # per-router switching overhead
pe_to_router_bw_gbs: 256.0 # PE_DMA ↔ router (= N × channel_bw)
hbm_to_router_bw_gbs: 256.0 # HBM_CTRL ↔ router (= N × channel_bw)
sram_to_router_bw_gbs: 128.0 # SRAM ↔ router
m_cpu_to_router_mm: 0.0 # M_CPU ↔ router distance
pe_dma_to_noc_bw_gbs: 256.0 # PE → router BW (= HBM slice BW, no bottleneck)
noc_to_pe_cpu_mm: 0.0 # router → PE_CPU distance (command path)
visualization:
emit_views: [system, sip, cube]
sip_ids: [0]
cubes: [0, 9, 15]