- Delete popcount.sv, argmax_update.sv, cam_core.sv, match_engine.sv
- Remove obsolete VERILOG_SOURCES entries from Makefile
- Update comment in cam_top.sv to reference match_engine_pipeline
- Add verilator to devenv.nix for simulation support
- Replace NOISE_GEN_BITS/NOISE_SAMPLE_BITS parameters with unified NOISE_BITS
- Use xorshift128 (random128) instead of xorshift64 for PRNG
- Add flip_mask_next combinational helper for single-cycle mask computation
- Add random_enable signal to advance PRNG only on accepted noisy writes
- Simplify FSM by removing mask_group_idx counter
- Update parameter validation: GROUP_BITS (= HASH_BITS/NOISE_BITS) must equal 64
- Update ref_model.py and tests to match new seed convention: {seed, seed}
- Update Makefile and sweep_noise.py with renamed parameters
- Add random32, random64 and random128 xorshift PRNG modules
- Refactor cam_noisy FSM: split state register, next-state logic, and datapath into distinct blocks
- Rename state_q/state_d to curr_state/next_state for clarity
- Add MASK_GROUPS localparam and fix type casting in noise generation
- Update .gitignore to exclude docs/superpowers
- Move load_env_file, parse_timeout_seconds, build_remote_script, and
build_ssh_command to scripts/common.py
- Update remote_docker_run.py to import from common module
- Improves code organization and reusability
- Add verilator to dependencies
- Add configurable logging via QUIET/VERBOSE/COCOTB_LOG_LEVEL env vars
- Add optional warning suppression (SUPPRESS_VERILATOR_WARNINGS)
- Clean up and restructure Makefile
- Split cam_core into pure memory (cam_core.sv) and match engine (match_engine.sv)
- Add cam_params.svh with centralized parameter definitions (NUM_ROWS, HASH_BITS, LANES, etc.)
- Update cam_top.sv to use shared parameters and compose match_engine
- Update Makefile to include new match_engine module and correct Verilator define syntax
- Move habitat-baselines and habitat-lab from pip to conda environment
- Add cocotb and cocotb-tools to pyproject.toml dependencies
- Update ty environment roots to include hw/sim directory
- Add sim/sim_build to gitignore
- Create CLAUDE.md with project spec, coding guidelines, and directory structure
- Update uv.lock to reflect dependency changes
- Add FramePacket dataclass to encapsulate per-image pipeline state
- Rename internal methods with underscore prefix convention
- Replace separate filter_batch/crop_batch with unified process_batch method
- Update notebook to use new HashPipeline API
- Add Owlv2ForObjectDetection and Owlv2Processor imports to model_loader
- Refactor load_dino_model to return tuple of processor and model
- Rewrite generate_proposals_batch to group images by bbox count for efficient batching
- Add _normalize_single_bbox_list helper for bbox normalization
- Update verification.py to use new pipeline architecture with detect/segment/filter/crop steps
- Add crop_batch method to HashPipeline for cropping images using OWLv2 detection boxes
- Integrate crop_batch into pipeline forward pass (extract_hash and extract_features)
- Move extract_masked_region from compressors/proposal/utils.py to utils/image.py
- Add crop_image_by_bbox utility function in utils/image.py
- Update type annotations to use dict[str, Any] instead of bare dict
- Update .justfile to add memory server command
- Update marimo dependency to >=0.22.0
- Update nvidia CUDA package markers for platform compatibility
- Replace bbox-prompted segmentation with OWLv2 text-guided object detection
- Refactor HashPipeline from nn.Module to plain class with modular stage methods
- Add detect_batch, segment_batch, filter_batch for explicit pipeline stages
- Rename forward to forward_batch with text_labels API instead of bboxes
- Add mask_scoring_config, score_threshold, postprocess_threshold configuration
- Update model_loader to expose Dinov2Model type annotation
- Refactor model_loader.py: improve return type annotations from tuple[Any, Any] to tuple[AutoImageProcessor, AutoModel]
- Refactor proposal/core.py: add input validation for mask array dimensionality, handle 2D masks and batch dimensions gracefully
- Refactor proposal_segament.ipynb: replace inline model loading with centralized load_owlv2_model() and load_sam_model() functions, use batched detect_objects_batch() and generate_proposals_batch() APIs
- switch OWLv2 loader return type to Owlv2ForObjectDetection
- add detect_objects and detect_objects_batch with two-stage score filtering
- add DetectionResult typed dict and conversion helper for post-processed outputs
- export new detection APIs from proposal module
- Replace SAM2AutomaticMaskGenerator pipeline with Sam2Processor+Sam2Model
- Freeze SAM model parameters at load time, removing torch.no_grad() at call sites
- Rewrite proposal/core.py to use bbox prompts instead of automatic point sampling
- Add bboxes parameter to all HashPipeline public methods (forward, forward_dataset, extract_features, extract_features_dataset)
- Extract mask filtering logic (_filter_masks) from proposal into pipeline
- Rename object_score/ to filter/
- Add load_owlv2_model to model_loader
- Rename notebooks/test.py to habitat_sim_setup.py
