DFFRAM Compiler

Standard Cell Library based Memory Compiler using DFF/Latch cells.

✨ Quick Usage

You can try the Google Colaboratory, but also...

Get Docker, set it up nicely, then, in your terminal:

pip3 install --user --no-cache-dir --upgrade ./requirements.txt
./dffram.py -s 32x32

If all goes well, you've placed your first register file!

Overview

The objective of this project is to develop a DFF/Latch-based RAM, Register File and Cache custom compilation flow that utilizes standard cell libraries following a standard ASIC (Application Specific Integrated Circuit) implementation approach. Different views (HDL netlist, HDL functional models, LEF, GDS, Timing, …) are all generated for a given size configuration.

The layout targets highly compact designs (85%+) as the cells are placed on the floor plan using a custom placer. Moreover, the custom placer ensures that the routing will be relatively simple. Currently, the compiler uses OpenROAD routers to route the macros with great success.

The Compiler relies on basic building blocks to construct the layout of different RAM/RF/Cache configurations. Check the compiler documentation for more info. The following shows how a 32x32 memory (DFF based) is constructed.

The generated layouts by the DFFRAM compilers for RAM32 as well as its building blocks are as follows:

BYTE

WORD (32-bit)
RAM8 (32x8)
RAM32 (32x32)

Currently, the can compiler generate the layout of the following configurations:

RAM
- 32 words with byte write enable (1RW and 1RW1R).
- 128 words with byte write enable (1RW and 1RW1R).
- 256 words with byte write enable (1RW and 1RW1R).
- 512 words with byte write enable (1RW and 1RW1R).
- 1024 words with byte write enable (1RW and 1RW1R).
- 2048 words with byte write enable (1RW and 1RW1R).
Register File
- 32 x 32-bit words (2R1W)

The layout of 2048x32 (8 Kbytes) RAM as generated by the compiler is shown below:

The OpenLane/ folder will contain good known OpenLane configurations to build DFFRAM different macros.

File Structure

.github contains files for GitHub actions.
docs/ contains documentation (😮)
rtl/ contains RTL RAM file generators for benchmarking and comparison purposes.
platforms/ contains PDK-specific files:
- <pdk-name>/
  - <scl-name>/
    - _building_blocks/ contains a hierarchy of building blocks supported by the compiler.
placeram/ is the custom placer Python module.
scripts/ has assisting scripts used by the flow.
dffram.py is the compilation flow going from building blocks to LVS.

Comparisons

The following table compares the areas and bit densities of RAM macros generated using different means.

Size¹	OpenRAM²		DFFRAM Compiler		DFFRAM/OpenLane		RTL/OpenLane
Size¹	Dim WxH (μm)	Bit Density (bits/mm²)	Dim WxH (μm)	Bit Density (bits/mm²)	Dim WxH (μm)	Bit Density (bits/mm²)	Dim WxH (μm)	Bit Density (bits/mm²)
512 bytes	N/A	N/A	395.6 x 388.96	26,619	TBD	TBD	680.25 x 690.97	8,714
1 kbytes	386 x 456	46,541	788.44 x 394.4	26,344	TBD	TBD	1,050 x 1,060	7,360
2 kbytes	659.98 x 398.18	62,372	793.5 x 783.36	26,358	TBD	TBD	1,439.615 x 1,450.335	7,847
4 kbytes	670.86 x 651.14	75,014	1,584.24 x 788.8	26,196	TBD	TBD	2,074 x 2,085	7,578
8 kbytes	N/A	N/A	1,589 x 1,572	26,229	TBD	TBD	2,686.610 x 2,697.330	9,043

¹ All support 32-bit word reads and 1, 2, and 4 bytes writes.
² Values are based on the original layout produced by the compiler. OpenRAM macros are typically wrapped to be useful w/ automated PnR ASIC flows.

⚖️ Copyright and Licensing

Licensed under the Apache License, Version 2.0 (the "Open Source License"); you may not use this file except in compliance with the Open Source License. You may obtain a copy of the Open Source License at the root of this repository (see the file 'License') or at

http://www.apache.org/licenses/LICENSE-2.0