ffp | iCE40 FPGA , SPI flash , and CMSIS-DAP SWD programmer
kandi X-RAY | ffp Summary
kandi X-RAY | ffp Summary
FFP is a dead-simple USB to bidirectional SPI bridge for programming iCE40 FPGAs and SPI flashes. The hardware is an STM32F042 and not much else. Firmware and host software is written in Rust. The firmware additionally supports SWD via the CMSIS-DAP v1 and v2 protocols, so many debuggers such as probe-rs, OpenOCD, and pyOCD are able to use an FFP to debug and program Cortex-M and other microcontrollers. See software/ for the host-side software, firmware/ for the embedded device firmware, and hardware/ for the hardware design files.
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ffp Examples and Code Snippets
Lines of Code : 8License : Non-SPDX (NOASSERTION)
Copy ______
3v3 --|1 2|-- FLASH DI / FPGA DO / SWDIO
GND --|3 4|-- CLK / SWCLK
GND --|5 6|-- CS / SWO
x-|7 8|-- FPGA nRST
GND --|9 10|-- FLASH DO / FPGA DI / nRESET
------
Community Discussions
Trending Discussions on ffp
QUESTION
The macro NAN from math.h is quiet NAN:
ISO/IEC 9899:2011 (E) (emphasis added):
The macro
NAN
is defined if and only if the implementation supports quiet NaNs for the float type. It expands to a constant expression of type float representing a quiet NaN.
Quiet NaNs usually do not lead to raising of FP exceptions. Examples:
- ISO/IEC 9899:2011 (E) (emphasis added):
5.2.4.2.2 Characteristics of floating types
3 A quiet NaN propagates through almost every arithmetic operation without raising a floating-point exception; a signaling NaN generally raises a floating-point exception when occurring as an arithmetic operand.
- IEEE 754-2008 (emphasis added):
5.11 Details of comparison predicates Programs that explicitly take account of the possibility of quiet NaN operands may use the unordered-quiet predicates in Table 5.3 which do not signal such an invalid operation exception.
However:
llrintf()is neither arithmetic operation, nor unordered-quiet predicates in Table 5.3. Hence,5.2.4.2.2.3and5.11are not applicable.7.12.9.5 The lrint and llrint functions(for example) says nothing about whether FP exceptions are raised or not in case if input is quiet NaN.
Preliminary conclusion: Because of the general practice "quiet NaN does not lead to raising FP exceptions" it can be concluded that the lrint and llrint functions should not lead to raising FP exceptions if input is quiet NaN.
Practice:
Code (t125.c):
...ANSWER
Answered 2021-Mar-30 at 16:13The OP was under impression that term conversion implies only implicit conversion and explicit conversion (cast operation) (see C11 6.3 Conversions). Meaning that OP was under impression that term function is not a conversion.
However, C11 F.3 Operators and functions (emphasis added) explicitly states that:
The
lrintandllrintfunctions inprovide the IEC 60559 conversions.
Hence, yes, C11 F.4 Floating to integer conversion (emphasis added) answers the question:
F.4 Floating to integer conversion
1 ... Otherwise, if the floating value is infinite or NaN or if the integral part of the floating value exceeds the range of the integer type, then the ‘‘invalid’’ floating-point exception is raised and the resulting value is unspecified. ...
QUESTION
The code given below shows different results, depending on -O or -fno-inline flags. Same (strange) results for g++ 10.1 and 10.2 and clang++ 10 on x86. Is this because the code is ill-formed or is this a genuine bug?
The "invalid" flag in Nakshatra constructor should be set whenever it's nakshatra (double) field is >= 27.0. But, when initialized via Nakshatra(Nirayana_Longitude{360.0}), the flag is not set, even though the the value after scaling becomes exactly 27.0. I assume that the reason is that the argument of 360.0 after scaling becomes 26.9999999999999990008 (raw 0x4003d7fffffffffffdc0) in 80-bit internal register, which is < 27.0, but, being stored as 64-bit double, becomes 27.0. Still, this behavior looks weird: the same nakshatra seems to be simultaneously <27.0 and >= 27.0. Is it the way it's supposed to be?
Is it the expected behaviour because my code contains UB or otherwise ill-formed? Or is it a compiler bug?
Minimal code to reproduce (two .cpp files + one header, could not reproduce with less):
main.cpp:
...ANSWER
Answered 2020-Dec-04 at 21:39Without -ffloat-store, GCC targeting x87 does violate the standard: it keeps values un-rounded even across statements. (-mfpmath=387 is the default for -m32). Assignment like double x = y; is supposed to round to actual double in ISO C++, and probably also passing a function arg.
So I think your code is safe for ISO C++ rules, even with the FLT_EVAL_METHOD == 2 that GCC claims to be doing. (https://en.cppreference.com/w/cpp/types/climits/FLT_EVAL_METHOD)
See also https://randomascii.wordpress.com/2012/03/21/intermediate-floating-point-precision/ for more about the real-world issues, with actual compilers for x86.
https://gcc.gnu.org/wiki/x87note doesn't really mention the difference between when GCC rounds vs. when ISO C++ requires rounding, just describes GCC's actual behaviour.
QUESTION
My intention is to do preparation of sound wave file, arrange train process, and test process via sound.c. ran into error during compiling darknet. need your help!
make: gcc: command not found Makefile: 175: recipe for target 'obj/sound.o' failed make: *** [obj/sound.o] Error 127 UBUNTU LTS 18.04 CUDA 11.1
@wbcalex-desktop:~$ sudo apt install gcc
[sudo] password for wbcalex:
Reading package lists... Done
Building dependency tree
Reading state information... Done
gcc is already the newest version (4:7.4.0-1ubuntu2.3).
The following package was automatically installed and is no longer required:
linux-hwe-5.4-headers-5.4.0-47
Use 'sudo apt autoremove' to remove it.
0 upgraded, 0 newly installed, 0 to remove and 0 not upgraded.
desktop:~$ cd darknet_tmp
desktop:~/darknet_tmp$ make
gcc -Iinclude/ -I3rdparty/stb/include -DGPU -I/usr/local/cuda/include/ -DCUDNN -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DGPU -DCUDNN -I/usr/local/cudnn/include -fPIC -c ./src/sound.c -o obj/sound.o
make: gcc: Command not found
Makefile:175: recipe for target 'obj/sound.o' failed
make: *** [obj/sound.o] Error 127
ANSWER
Answered 2020-Nov-30 at 18:34PATH variable isn't updated correctly, $PATH is not makefile syntax. Fix:
QUESTION
Say we're Amazon, and we receive a customer order in the form of this nested dict down here 👇
...ANSWER
Answered 2020-Nov-30 at 16:39Not sure why you use pandas - you can do this using simple python with no imports whatsoever:
QUESTION
I need to paste first picture on second and save new picture as new file. How I can do it? My not-working code in the bottom.
...ANSWER
Answered 2020-Nov-16 at 19:12I didn't see anything wrong but I think you can save the image without using the open method.
Also, you can paste on your mat image and save it as another image with save
QUESTION
I am a newbie in c++, and heard that libraries like eigen, blaze, Fastor and Xtensor with lazy-evaluation and simd are fast for vectorized operation.
I measured the time collapsed in some doing basic numeric operation by the following function:
(Fastor)
...ANSWER
Answered 2020-Oct-11 at 10:40The reason the Numpy implementation is much faster is that it does not compute the same thing as the two others.
Indeed, the python version does not read z in the expression np.sin(x) * np.cos(x). As a result, the Numba JIT is clever enough to execute the loop only once justifying a factor of 100 between Fastor and Numba. You can check that by replacing range(100) by range(10000000000) and observing the same timings.
Finally, XTensor is faster than Fastor in this benchmark as it seems to use its own fast SIMD implementation of exp/sin/cos while Fastor seems to use a scalar implementation from libm justifying the factor of 2 between XTensor and Fastor.
Answer to the update:
Fastor/Xtensor performs really bad in exp, sin, cos, which was surprising.
No. We cannot conclude that from the benchmark. What you are comparing is the ability of compilers to optimize your code. In this case, Numba is better than plain C++ compilers as it deals with a high-level SIMD-aware code while C++ compilers have to deals with a huge low-level template-based code coming from the Fastor/Xtensor libraries. Theoretically, I think that it should be possible for a C++ compiler to apply the same kind of high-level optimization than Numba, but it is just harder. Moreover, note that Numpy tends to create/allocate temporary arrays while Fastor/Xtensor should not.
In practice, Numba is faster because u is a constant and so is exp(u), sin(u) and cos(u). Thus, Numba precompute the expression (computed only once) and still perform the sum in the loop. The following code give the same timing:
QUESTION
Below is my code:
...ANSWER
Answered 2020-Jul-20 at 11:44you have to change these 3 lines.
QUESTION
I am quite new to python so please bear with me.
Currently, this is my code:
...ANSWER
Answered 2020-Aug-11 at 03:53Why not use an if-statement at the beginning? Try this:
instead of:
QUESTION
Here is my code so far:
...ANSWER
Answered 2020-Jul-27 at 13:46Change:
QUESTION
Please bear with me as i am quite new to python.
Currently, this is my code:
...ANSWER
Answered 2020-Jul-27 at 06:46You can use plt.show(block=False) which makes show return immediately after the window is shown.
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