Algorithmic Multi-ported Memory(MEM) - Comprehensive Techniques Guideline

Student: Chun-Feng Chen
Advisor: Bo-Cheng Charles Lai
Mar. 8th, 2018
NCTU Institute of Electronics – Parallel Computing System Lab (NCTU PCS) Hsinchu, R.O.C
Towards Algorithmic Multi-ported Memory :
Techniques and Design Trade-offs

•Introduction
•Background
•Non-Table-Based Approaches
•Table-Based Approaches
•Performance and Impact of Design Factors
•Conclusions
•References
Chun-Feng Chen NCTU_IEE - PCS Lab
Outline
2

Introduction
• Algorithmic Multi-ported Memory (AMM)
• Multi-ported memories are important functional modules in modern digital systems
• E.g. shared cache in multi-core processors, routing tables of switches, etc.
• AMM composes simple SRAMs and logic to support multiple reads and writes
• Potential to attain better performance than circuit-based approaches (CMM)
• Most of the previous works on FPGA
• Laforest, Charles Eric, et al. "Efficient Multi-ported Memories for FPGAs" [ACM 2010]
• Charles Eric Laforest, et al. "Multi-ported Memories for FPGAs via XOR" [ACM 2012]
• Charles Eric Laforest, et al. "Composing Multi-Ported Memories on FPGAs" [ACM 2014]
• Jiun-Liang Lin, et al. "BRAM Efficient Multi-ported Memory on FPGAs" [VLSI-DAT 2015]
• Jiun-Liang Lin, et al. "Efficient Designs of Multi-ported Memory on FPGAs" [TVLSI 2016]
• Kun-Hua Huang, et al. "An Efficient Hierarchical Banking Structure for Algorithmic Multi-
ported Memory on FPGAs" [TVLSI 2017]
• Sundar Iyer, et al. "Algorithmic Memory Brings an Order of Magnitude Performance Increase
to Next Generation SoC Memories" [DesignCon 2012]
4

Motivation
– FPGA Limits AMM Exploration
• The limited resource on FPGA constrains the AMM exploration
• Limited number of BRAMs, F/F, slice LUTs, etc
• Unable to explore important design factors of AMM
• Number of ports, memory depth, banking structures
• AMM has more significant benefit for greater depth
• E.g. from 512K to 16M-depth, AMM 4R1W attains 1.25% to 36.47% shorter latency better than
circuit-based approaches
• BRAM size is fixed
• 1K depth of 32-bit data width
• Unable to explore impact of different bank sizes
• E.g. choose proper banking structure for 2R1W can enhance latency/area/power up to
9.53%/56.86%/33.39%
• BRAM port configuration is fixed
• dual-port 2RW mode
• Unable to explore impact of different bank port configurations (4R1W, 2R2W, etc)
• E.g. choose proper building memory cell for 8R4W can enhance the area/power up to
70.0%/6.37x
5

Our Contributions
• Implement all the AMM designs on 45nm technology
• Use SRAM as building memory cell
• Explore important design factors of AMM
• Different AMM designs, memory depth, port configurations, banking
structures, building memory cells, etc.
• Extensive experiments and comprehensive analysis
• Summarize observations into design guidelines
6

• Non-table-based schemes
• Duplicate memory module
• E.g. NTRep-Rd [ACM 2010]
• Table-based schemes
• Adopt lookup tables to track the
stored up-to-date data address
• E.g. TBLVT [ACM 2010]
Algorithmic Multi-ported Memory
(AMM) Techniques Categorize
8

• Non-table-based approaches
• Use multiple banks to support multiple accesses
• Store parity data to support multiple reads and enable multiple
writes [VLSI-DAT’15, TVLSI’16, TVLSI’17]
• HB-NTX-RdWr can scale the number of ports with a systematic flow
[TVLSI’17]
• Table-based approaches
• Use multiple memory modules to support multiple accesses
• Use lookup tables to avoid module conflict and track the most up-to-
date values [VLSI-DAT’ 15, TVLSI’ 16, TVLSI’ 17]
AMM - Previous Proposed Designs
9

Non-Table-Based
Approaches
Multiple Reads:
(1) Non-Table-Based Replication (NTRep-Rd)
(2) Non-Table-Based XOR (NTX-Rd)
(3) Hierarchical Banking Non-Table XOR-Based (HB-NTX-Rd)
Multiple Write:
(1) Non-Table-Based XOR (NTX-Wr)
(2) Hierarchical Banking Non-Table XOR-Based (HB-NTX-Wr)
Multiple Reads and Writes:
(1) Hierarchical Banking Non-Table XOR-Based (HB-NTX-RdWr)

• A mR1W memory module of
NTRep-Rd technique
• Duplicate memory modules to
support multiple read ports
• Only one write port connects each
memory module
[7] LaForest, Charles Eric, and J. Gregory Steffan. "Efficient Multi-ported Memories for FPGAs." Proceedings of the 18th Annual
ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGAs). ACM, 2010.
Non-Table-Based Replication Multiple
Reads (NTRep-Rd) - [ACM’10, TRETs’14]
12

• A 2R1W memory module of NTX-
Rd technique
• Write request
• W0 stores directly to BANK0 and
read D0 from BANK1
• Update the XOR-BANK
• Read request
• R1 reads directly
• R0 reads the other banks to
recover correct data
• NTX-Rd support two mode
• Case 1: 3R (no write request)
• Case 2: 2R1W (one write request)
[13] Sundar Iyer, Shang-Tse Chuang, and Co-Founder & CTO Memoir Systems. " Algorithmic Memory: An Order of Magnitude
Performance Increase for Next Generation SoCs." DesignCon. (http://www.designcon.com), 2012.
Non-Table-Based XOR Multiple Reads
(NTX-Rd) - [DesignCon.’12]
W0’ = (W0 D0)
R0 = (W0 D0) D0
W0’
14

Hierarchical Banking Non-Table XOR-Based
Multiple Reads (HB-NTX-Rd) - [TVLSI’17]
[12] Lai, Bo-Cheng Charles, and Kun-Hua Huang. "An Efficient Hierarchical Banking Structure for Algorithmic Multiported Memory on
FPGAs." IEEE Transactions on Very Large Scale Integration (VLSI) Systems 25.10 (2017): 2776-2788.
• 4 read and 1 write memory
• Scale the B-NTX-Rd to more reads in a
hierarchical structure
• Use the 2R1W/3R as building modules
• Case 1: 5R (no write request)
• Three reads access BANK0
• Other two reads access the other banks
• Case 2: 4R1W (one write request)
• W0 and two reads access BANK0
• Other two reads access the other banks
• W0 stores directly, and reads BANK1 for
updating XOR-BANK
16

• A 1R2W memory module of NTX-Wr technique
• Duplicate memory modules and store XOR-encoded values to support multiple
read and writes
[9] LaForest, Charles Eric, et al. "Multi-ported Memories for FPGAs via XOR.” Proceedings of the ACM/SIGDA International Symposium on
Field Programmable Gate Arrays (FPGAs). ACM, 2012.
Non-Table-Based XOR Multiple Writes
(NTX-Wr) - [ACM’12, TRETs’14]
W0’ = (W0 D0) D0
W1’ = (W1 D1) D1
W0’ W0’
W1’
W1’
18

Hierarchical Banking Non-Table XOR-Based
Multiple Writes (HB-NTX-Wr) - [TVLSI’17]
(a) Non-Conflict-Write case (b) Conflict-Write case
W0’ = W0 Ref0
W1’ = W1 Ref1
Ref1new = W1 (D0 Ref1cur)
W1’’ = D1 Ref1new
W1’
W0’
W1’’
20

• 2 read and 2 write memory
• Integrates HB-NTX-Rd and HB-NTX-Wr to enable multiple reads and writes
• Use HB-NTX-Rd 4R1W/5R as building memory modules
(b) Conflict-Write case(a) Non-Conflict-Write case
Hierarchical Banking Non-Table XOR-Based Multiple
Reads and Writes (HB-NTX-RdWr) - [TVLSI’17]
22

• The top-down flow increases read ports with HB-NTX-Rd, while the left-
right flow increases write ports with HB-NTX-Wr
HB-NTX-RdWr Systematic Flow
- [TVLSI’17]
23

Table-Based
Approaches
Multiple Reads and Writes:
(1) Table-Based Live Value Table (TBLVT)
(2) Table-Based Remap Table (TBRemap)
(3) Enhancing Table-Based with Reduce Lookup Tables
(TBLVT_HB-NTX-RdWr)

[7] LaForest, Charles Eric, and J. Gregory Steffan. "Efficient Multi-ported Memories for FPGAs." Proceedings of the 18th Annual
ACM/SIGDA International Symposium on Field Programmable Gate Arrays (FPGAs). ACM, 2010.
Table-Based Live Value Table (TBLVT)
- [ACM’10, TRETs’14]
• Write request
• Dedicate a write data to a certain
memory module
• Lookup table (LVT) traces the latest
location
• Read request will query the LVT first
and then access the data from correct
memory location
• Design of the LVT size:
• log2(#NumModules) x MemoryDepth
26

[11] Lai, Bo-Cheng Charles, and Jiun-Liang Lin. "Efficient Designs of Multi-ported Memory on FPGAs." IEEE Transactions on Very Large
Scale Integration (VLSI) Systems 25.1 (2017): 139-150.
Table-Based Remap (TBRemap)
– [VLSI-DAT’15, TVLSI’16]
• Remap functions:
• Apply banking structure designs
• All the reads and writes need to check
remap table to determine which
memory bank to access
• Use a HWC to distribute the multiple
write into writes, and a remap table to
track the latest location
• Design of the Remap size:
• ([log2(#DataBanks + 1)] – 1) x
MemoryDepth
28

Enhancing Table-Based Design with
Reduce Lookup Table
• NTRep-Rd mR1W modules replaced
by HB-NTX-RdWr mRnW modules
• Reduce lookup table size while uses
less modules, to alleviate the routing
complexity for latency critical path
• Example: A 2R4W 8K-depth memory
• Original TBLVT needs four NTRep-Rd
2R1W as building modules
• LVTSize = 2-bit x 8K-depth
• Enhance TBLVT needs two HB-NTX-
RdWr 2R2W as building modules
• LVTSize = 1-bit x 8K-depth
30

Performance and Impact
of Design Factors
A. Experiments Setup
B. Circuit-level vs. Algorithmic Scheme
C. Overall Performance and Cost (Non-Table-Based vs. Table-Based)
D. Impact of Banking Structure
E. Scalability with Memory Depths and Number of Ports
F. Proper Tradeoff Between Circuit-level and Algorithmic Memory
F.(1) Different Basic SRAM Modules
F.(2) AMM Designs with Higher Port Counts Circuit-level Modules

– Read/Write Path Logic
• The block diagram of an AMM architecture, including write-path logic, read-path
logic, and building memory cells
• Write-path performs data manipulation design, e.g. replication, and lookup table… etc.
• Read-path performs retrieve the data from memory cells and decoding correct data
33
= Design Compiler synthesis RTL with TSMC 45nm

- Memory Cells (SRAM)
• The block diagram of an AMM architecture, including write-path logic, read-path
logic, and building memory cells
• Memory cells are composed by SRAMs, e.g. single-port or dual-port mode
34
= CACTI integrated memory model to estimate the
performance of different SRAM modules with TSMC 45nm

A. Experiments Setup - Algorithmic
Multi-ported Memory Architecture
• By combining the synthesis results of read-path and write-path logic, and
estimation from CACTI, we can evaluate the overall performance and cost
of an AMM design
35
= Overall performance of an AMM designs++

B. Circuit-level vs. Algorithmic Schemes
– 2RnW (Latency)
37
l CMM has shorter latency for shallow memory depth (< 64K)
AMM has short latency for greater memory depth (> 128K)
l AMM is more scalable when increasing memory depth
For 2R8W, from 16K to 16M, the latency increases by:
HB-NTX-RdWr (1.52x), TBLVT_B-NTX-Rd (2.85x), CMM (23.46x)
l Non-table designs have shorter latency than table-based
For 2R8W, HB-NTX-Rd attains 4.23% to 95.09% shorter latencies
than TBLVT_B-NTX-Rd from 16K to 16M

– mR1W (Area)
38
l Non-table-based
AMM CMM
l 2R1W attains 6.38% to 36.2%
l 4R1W attains 15.27% to 67%
l 8R1W attains 59.33% to 3.01x smaller area when
compared with CMM

– 2RnW (Area)
39
l Non-table-based
CMM
l Table-based designs still attain smaller area over CMM
For 2R8W TBLVT_B-NTX-Rd, can attains 2.01% to
22.79% smaller area from 64K to 16M
l Table-based memory cell ,
table-based non-table-based

– 2RnW (Power)
40
l AMM has lower power (ever for non-table-based designs)
For AMM, access data
random bank
access data AMM
l For 2R8W HB-NTX-RdWr, can attains 45.59% to 2.1x
from 512K to 16M
l For 2R8W TBLVT_B-NTX-Rd, can attains 3.29% to
4.71x from 1K to 16M

C. Overall Performance and Cost
- Non-Table-Based vs. Table-Based (Latency)
l The non-table-based schemes have shorter access
latencies with simple logic operations; table-based
schemes are impacted by routing path to lookup table
l The latency of AMM is mainly determined by the SRAM
modules for greater memory depth
For example: for 1R2W, 1K to 1M, memory cells account:
NTX-Wr: 28.82% to 71.55% of overall latency
TBLVT: 26.94% to 69.61% of overall latency
42

- Non-Table-Based vs. Table-Based (Area)
l The area of AMM is mainly determined by the SRAM
modules
For example, 1R2W, 1K to 1M, memory cells account:
NTX-Wr: 92.52% to 99.99% of overall area
TBLVT: 93.11% to 99.99% of overall area
l Table-based ,
table-based non-table-based
43

- Non-Table-Based vs. Table-Based (Power)
44
l The power of AMM is mainly determined by the SRAM
modules
For example, for 1R2W, 1K to 1M, memory cells account:
NTX-Wr: 89.27% to 99.97% of overall power
TBLVT: 88.9% to 99.97% of overall power
l Table-based , table-
based non-table-based

– Non-Table-Based (Latency)
l Banking structure is a tradeoff between memory cell and logic
l The best banking structure would be different according to
designs
l For example, for 2R1W, the 32-bank has the shortest latency
among all the other banking structures
46

– Non-Table-Based (Area)
47
l Area efficiency: (Area)/(data size), data
memory cell area
l 8-bank has the smaller area among all the other designs
l Area of memory cell is the dominant factor of overall
area:
Logic only occupies 0.0782% (1bank) to 8.87%(256bank)
of overall area
2
1.502
1.253 1.13
1.17 1.246
1.346
1.566
1.80

– Non-Table-Based (Power)
48
l Power efficiency: (Power)/(data size), data
power access
l 8-bank has the lower power among all the other designs
l Power of memory cell is the dominant factor of overall
power:
Logic only occupies : 0.201% (1bank) to 10.653%
(256bank) of overall power
1.68
1.29
1.17
1.13 1.27
1.46
1.74
1.866
2.01

E. Scalability with Memory Depths and
Number of Ports - Latency
l The AMM performance is mainly determined by
the SRAM modules
l For 2R4W HB-NTX-RdWr, (64K to 128K)
& (256K to 512K) latency
l For 2R4W HB-NTX-RdWr, (128K to 256K)
latency
50
2K 2RW
4K 2RW

Number of Ports - Area
51
l The AMM performance is mainly determined
by the SRAM modules
l HB-NTX-RdWr attains smaller area than NTX-
Wr_B-NTX-Rd
l For 2R8W, HB-NTX-RdWr attains 11.21% to
2.33x smaller area from 16K to 1M, than NTX-
Wr_B-NTX-Rd

Number of Ports - Power
52
l The AMM performance is mainly determined by
the SRAM modules
l HB-NTX-RdWr attains lower power than NTX-
Wr_B-NTX-Rd
l For 2R8W, HB-NTX-RdWr attains 9.51% to
55.39% lower power from 16K to 1M, than
NTX-Wr_B-NTX-Rd

- Latency
For a wide range of sizes, all these basic SRAM
modules pose very similar performance and cost
54

- Area
55
For a wide range of sizes, all these basic SRAM
modules pose very similar performance and cost

- Power
l For a widely range of sizes, power
consumption: 2RW > 1R1W/2R > 1R1W
l AMM 2RW
2RW + 1R1W (power)
56

F.(2) AMM Designs with Higher Port
Counts Circuit-level Modules - Latency
l CMM could outperform AMM in certain configurations
l Can we attain better performance by properly choosing
SRAM modules?
l Apply three different SRAM modules (2RW, 2R2W, 2R4W),
we use 4R2W as an example
l For 4R2W, AMM with 2RW SRAMs attains 6.08% to 2.032x
faster latencies from 1K to 16M than AMM with 2R2W
l This is because latency of HB-NTX-RdWr is mainly
determined by the SRAM module, and 2RW is faster (than
2R2W and 2R4W)
57

Counts Circuit-level Modules - Area
l But using more complex SRAM does provide
benefit on area and power
l AMM
(e.g. 4R2W: 54
2 )
l For 4R2W, AMM with 2R2W SRAM attains
30.28% to 71.43% smaller area from 1K to 16M
than AMM with 2RW
58

Counts Circuit-level Modules – Power
59
l But using more complex SRAM does provide
benefit on area and power
l AMM
(e.g. 4R2W:
54 2 )
l For 4R2W, AMM with 2R2W SRAM attains
2.52x to 3.42x lower power from 1K to 16M
than AMM with 2RW

Summary of Experiments on AMM
Studies
• AMM does attain superior performance (latency/ area/ power) than CMM, the
benefits become more significant for designs with more ports and greater depth
• The performance of AMM is mainly determined by the algorithmic logics when
memory depth is shallow. The building SRAM modules will become the main
performance factor for memory with great depth.
• Non-table-based AMMs have shorter latencies when compared with table-based
designs. Table-based AMMs pose smaller area and lower power consumption than
non-table-based AMMs
• Proper banking structure would enhance the performance while excessively
aggressive banking could induce significant overhead and performance hit
• Choosing proper SRAM with higher port counts as building modules could enhance
the performance (area/ power) of AMM designs
61

Conclusions
• Most of the previous works of AMM were conducted on FPGA-based
platforms, is implemented by composing multiple BRAMs and logic slices
LUT (lookup table)
• This thesis aims to comprehensive analysis and exploration the algorithmic
multi-ported memory on ASICs
• Different basic SRAM modules
• Scalability with memory depths and number of ports for AMM designs
• Applying banking structures for AMM designs
• Circuit-level schemes vs. Algorithmic schemes for different port configures
• Choosing proper SRAM modules with higher port counts can enhance the
performance of AMM designs
62

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