To replicate the cpu.out file shown below (though without my comments),
use this cpu.hdl, which passes all the tests.
Now, my question is, at clock cycle 3+, 4, and 4+. Notice that DRegise (D register) is not updated, despite the command was "D=A-D", why is that?
|time| inM | instruction |reset| outM |writeM |addre| pc |DRegiste|
a-instruc | store the number "12345"
|0+ | 0|0011000000111001| 0 | 0| 0 | 0| 0| 0 |
|1 | 0|0011000000111001| 0 | 0| 0 |12345| 1| 0 |
c-instru | comp: "A" | dest: "D" | jump: "no jump" | "D=A"
|1+ | 0|1110110000010000| 0 | 12345| 0 |12345| 1| 12345 |
|2 | 0|1110110000010000| 0 | 12345| 0 |12345| 2| |
a-instruc | "23456"
|2+ | 0|0101101110100000| 0 | -1| 0 |12345| 2| 12345 |
|3 | 0|0101101110100000| 0 | -1| 0 |23456| 3| 12345 |
c-instruc | comp: "A-D" | dest: "D" | jump: "no jump" | "D=A-D"
|3+ | 0|1110000111010000| 0 | 11111| 0 |23456| 3| 11111 |
|4 | 0|1110000111010000| 0 | 12345| 0 |23456| 4| 11111 |
a-instruc | "1000" WHY DREGISTE NOT CHANGE? v^v^
|4+ | 0|0000001111101000| 0 | -11111| 0 |23456| 4| 11111 |
|5 | 0|0000001111101000| 0 | -11111| 0 | 1000| 5| 11111 |
If your cpu.hdl is passing all the tests, it is probably operating correctly.
As far as I can tell (it's been several years since I built my CPU), the Dreg is being updated correctly; it gets updated in the + cycles. Note that in cycle 3, its value is 12345, and in 3+ (after the processing of the D=A-D) it is 11111 (which is 23456-12345, as you would expect).
My best guess is that what is happening is that the simulator doesn't update the values of the outputs of the cpu in the + phases, but does show the internal state. So you see the Dreg change in the + phases, but "addre" (which isn't an internal register, it's the external address lines) only changes in the non-+ phases.
Related
I have a number of tables that looks as follows:
time | node | left |LP iter|LP it/n|mem/heur|mdpt |vars |cons |rows |cuts |sepa|confs|strbr| dualbound | primalbound | gap | compl.
0.0s| 1 | 0 | 100 | - | 1046k | 0 | 100 | 102 | 100 | 0 | 0 | 0 | 0 | -- | 9.999990e+05*| Inf | unknown
* 0.3s| 1 | 0 | 100 | - | LP | 0 | 200 | 102 | 100 | 0 | 0 | 0 | 0 | -- | 5.587300e+04 | Inf | unknown
12.0s| 1 | 0 | 239 | - | 1781k | 0 | 239 | 102 | 100 | 0 | 0 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
12.1s| 1 | 0 | 287 | - | 2595k | 0 | 239 | 102 | 935 | 835 | 1 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
66.8s| 1 | 0 | 422 | - | 3061k | 0 | 336 | 102 | 935 | 835 | 1 | 0 | 0 | 5.577800e+04 | 5.587300e+04 | 0.17%| unknown
89.4s| 1 | 0 | 481 | - | 3218k | 0 | 361 | 102 | 935 | 835 | 1 | 0 | 0 | 5.580100e+04 | 5.587300e+04 | 0.13%| unknown
89.5s| 1 | 0 | 579 | - | 3513k | 0 | 361 | 102 |1335 |1235 | 2 | 0 | 0 | 5.580100e+04 | 5.587300e+04 | 0.13%| unknown
100s| 1 | 0 | 715 | - | 3837k | 0 | 403 | 102 |1335 |1235 | 2 | 0 | 0 | 5.583250e+04 | 5.587300e+04 | 0.07%| unknown
I'm interested in recording the first numeric value in the gap column (second last column of the table). The gap column could either have Inf or x.xx% values in it. If all the values in the gap column are Inf, then I would simply record Inf, otherwise, I would like to record the first numeric value. For e.g. in the above table, the value that I would like to record is 0.17. I tried many different ways but couldn't achieve any success. It would be really great if someone could provide some guidance as to how to achieve the above-mentioned objective. Thanks !
You may use this awk solution:
awk -F '[[:blank:]]*\\|[[:blank:]]*' '
NR > 1 && (!v || v == "Inf") {
v = ($(NF-1) == "Inf" ? $(NF-1) : $(NF-1)+0)
}
END {
print v
}' file
0.17
I have the following panel data set with very large N (500,000) and small T (15 years). My dependent variable is Project1 or project 2. I want to estimate the likelihood of Project dependent on treated with year and village fixed effects. For the continuous dependent variable, I was using reghdfe.
The dependent variable is simply that when a village gets the project the dummy is equal to 1 and remains 1 for the subsequent years.
I am aware that I cannot use "probit" command in STATA as I have a panel. Can you suggest which model should I use?
| village | population | year | project_1 | project_2 | treated |
|---------|------------|------|-----------|-----------|-----------|
| A | 100 | 2001 | 0 | 0 | 0 |
| A | 100 | 2002 | 1 | 0 | 0 |
| A | 100 | 2003 | 1 | 0 | 1 |
| A | 100 | 2004 | 1 | 0 | 1 |
| A | 100 | 2005 | 1 | 0 | 1 |
| B | 200 | 2001 | 0 | 0 | 0 |
| B | 200 | 2002 | 0 | 0 | 1 |
| B | 200 | 2003 | 0 | 1 | 1 |
| B | 200 | 2004 | 0 | 1 | 1 |
| B | 200 | 2005 | 0 | 1 | 1 |
| C | 150 | 2001 | 0 | 0 | 0 |
| C | 150 | 2002 | 0 | 0 | 0 |
| C | 150 | 2003 | 0 | 0 | 0 |
| C | 150 | 2004 | 1 | 0 | 0 |
| C | 150 | 2005 | 1 | 0 | 1 |
| D | 175 | 2001 | 0 | 0 | 0 |
| D | 175 | 2002 | 0 | 0 | 0 |
| D | 175 | 2003 | 0 | 0 | 0 |
| D | 175 | 2004 | 0 | 0 | 1 |
| D | 175 | 2005 | 0 | 0 | 1 |
Your question has two parts. Which model of Logit and Probit is more appropriate for you, and how to implement the appropriate model in Stata. As #NickCox mentioned, the former is most appropriate for Cross Validated, and has received robust discussion there: Difference between logit and probit models
.
I have some hardware IPs that I need to synthesize. And the IP contains several generic parameters I can play with. Each combination of parameters gives me a different utilization report after synthesis and implementation.
So for example for two different configurations Design_1 and Design_2, I get the following in Vivado 2018.1. The 3rd line is the ratio of the values of Design_2 devided by values of Design_1.
So as you can see in this simple example, Design_2 has less Slice LUTs but slightly more F7 Muxes.
My question is how to conclude about the cost of each one? Should I privilege Slice LUTs or Registers ...etc?
+----------+-------------------+-----------------+------------------+----------+-------------------+-------------------+---------------+---------------------+----------------+------+------------+--------------+-------------+------------+----------+---------+------------+---------+---------------------------+-------------------------+-----------------------------+--------+--------+----------+---------+------------+-----------+---------+--------+---------+---------+-----------+----------+-----------+-------------+---------+----------+-----------+---------+
| Name | Slice LUTs | Slice Registers | F7 Muxes | F8 Muxes | Slice | LUT as Logic | LUT as Memory | LUT Flip Flop Pairs | Block RAM Tile | DSPs | Bonded IOB | Bonded IPADs | PHY_CONTROL | PHASER_REF | OUT_FIFO | IN_FIFO | IDELAYCTRL | IBUFDS | PHASER_OUT/PHASER_OUT_PHY | PHASER_IN/PHASER_IN_PHY | IDELAYE2/IDELAYE2_FINEDELAY | ILOGIC | OLOGIC | BUFGCTRL | BUFIO | MMCME2_ADV | PLLE2_ADV | BUFMRCE | BUFHCE | BUFR | BSCANE2 | CAPTUREE2 | DNA_PORT | EFUSE_USR | FRAME_ECCE2 | ICAPE2 | PCIE_2_1 | STARTUPE2 | XADC |
+----------+-------------------+-----------------+------------------+----------+-------------------+-------------------+---------------+---------------------+----------------+------+------------+--------------+-------------+------------+----------+---------+------------+---------+---------------------------+-------------------------+-----------------------------+--------+--------+----------+---------+------------+-----------+---------+--------+---------+---------+-----------+----------+-----------+-------------+---------+----------+-----------+---------+
| Design_1 | 34124 | 16913 | 1453 | 91 | 10272 | 31538 | 2586 | 9020 | 37 | 11 | 125 | 0 | 1 | 1 | 4 | 2 | 1 | 0 | 4 | 2 | 16 | 16 | 46 | 10 | 0 | 2 | 2 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| Design_2 | 34097 | 16913 | 1550 | 91 | 10189 | 31511 | 2586 | 9021 | 37 | 11 | 125 | 0 | 1 | 1 | 4 | 2 | 1 | 0 | 4 | 2 | 16 | 16 | 46 | 10 | 0 | 2 | 2 | 0 | 2 | 0 | 4 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 |
| -------- | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
| (2)/(1) | 0.999208768022506 | 1 | 1.06675843083276 | 1 | 0.991919781931464 | 0.999143889910584 | 1 | 1.00011086474501 | 1 | 1 | 1 | #DIV/0! | 1 | 1 | 1 | 1 | 1 | #DIV/0! | 1 | 1 | 1 | 1 | 1 | 1 | #DIV/0! | 1 | 1 | #DIV/0! | 1 | #DIV/0! | 1 | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! | #DIV/0! |
+----------+-------------------+-----------------+------------------+----------+-------------------+-------------------+---------------+---------------------+----------------+------+------------+--------------+-------------+------------+----------+---------+------------+---------+---------------------------+-------------------------+-----------------------------+--------+--------+----------+---------+------------+-----------+---------+--------+---------+---------+-----------+----------+-----------+-------------+---------+----------+-----------+---------+
It's depending on your needs, LUTs and F7 Muxes are differents physical cells in your FPGA. So even if you don't use its, its will be there.
If you have one ressource more critical than the other, you should try to minimize the utilisation of the critical ressource to simplify the place and route.
If you have nothing critical, I think the better is to use F7 Muxes first because Slice LUTs are more flexible for the rest of your design.
when I study the algorithms for string matching, I met the lecture notes says that for example the pattern is abaab, the Morris-Pratt table is:
| a | b | a | a | b |
| 0 | 0 | 1 | 1 | 2 |
I understand how this is generated but the KMP table given is:
| a | b | a | a | b |
| 0 | -1| 1 | 0 | 2 |
Can anyone help me understand why the 2nd table is like that?
Thanks/
I am very new to statsample and having some basic questions. With this sample data:
[[1, 2, 3, 3],[2, 3, 3, 5],[4, 1, 3, 4]]
I create a 4x4 statsample dataaset called ds and get the following output for each call:
puts ds.summary
gets
= Dataset 1
Cases: 3
Element:[actuals]
== Vector 3
n :3
n valid:3
factors:3
mode: 3
Distribution
+---+---+---------+
| 3 | 3 | 100.00% |
+---+---+---------+
Element:[mids]
== Vector 2
n :3
n valid:3
factors:1,2,3
mode: 2
Distribution
+---+---+--------+
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
| 3 | 1 | 33.33% |
+---+---+--------+
Element:[predicteds]
== Vector 4
n :3
n valid:3
factors:3,4,5
mode: 3
Distribution
+---+---+--------+
| 3 | 1 | 33.33% |
| 4 | 1 | 33.33% |
| 5 | 1 | 33.33% |
+---+---+--------+
Element:[prediction_error]
== Vector 5
n :3
n valid:3
factors:0,1,2
mode: 0
Distribution
+---+---+--------+
| 0 | 1 | 33.33% |
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
+---+---+--------+
Element:[uids]
== Vector 1
n :3
n valid:3
factors:1,2,4
mode: 1
Distribution
+---+---+--------+
| 1 | 1 | 33.33% |
| 2 | 1 | 33.33% |
| 4 | 1 | 33.33% |
+---+---+--------+
Which seems reasonable but then:
cm = ds.correlation_matrix
puts cm.summary
gets this, which is confusing:
Correlation Matrix
+------------------+---------+-------+------------+------------------+-------+
| | actuals | mids | predicteds | prediction_error | uids |
+------------------+---------+-------+------------+------------------+-------+
| actuals | 1.000 | -- | -- | -- | -- |
| mids | -- | 1.000 | -- | -- | -- |
| predicteds | -- | -- | 1.000 | -- | -- |
| prediction_error | -- | -- | -- | 1.000 | -- |
| uids | -- | -- | -- | -- | 1.000 |
+------------------+---------+-------+------------+------------------+-------+
You created a dataset with nominal vectors, not scalar ones. So, correlations between not numeric vectors is always 0.