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zg3_sR*5gTxf(_n4;;(v1x^+ow@$mGUXoLx+&3S;#XNY0c;j_xxAc3>`!)<_D`?ZK; z)$fgMvYF)Yke7lIJ8A)4l^6g)b* zIQ`Mw@OUyW#hF}gqel;*T@2TyOV9!s6T_jz(DNh1#pDL6F5QrZo(`aB(e_b!<%_9{ zJVw?nmkiw@uol~pwKi=1pt, - node distance=5cm and 5cm, - on grid, - auto] - % States - \node[state, initial] (reset) {RESET}; - \node[draw=none,fill=none] (any) [below=2cm of reset] {ANY}; - \node[state] (init) [right=of reset] {INIT}; - \node[state] (zqcal) [below=of init] {ZQ Calibration}; - \node[state, accepting] (idle) [right=of init] {IDLE}; - \node[state] (writelevel) [above=of idle] {WRITE LEVELING}; - \node[state] (refresh) [right=of idle] {REFRESH}; - \node[state] (activation) [below=of idle] {ACTIVATION}; - \node[state] (bankactive) [below=of activation] {BANK ACTIVE}; - \node[state] (readop) [below right=of bankactive] {READ OP}; - \node[state] (writeop) [below left=of bankactive] {WRITE OP}; - \node[state] (prechrg) [below right=of readop] {PRE-CHARGING}; - % Transitions - \path[->, line width=0.2mm, >=stealth] - (reset) edge node {} (init) - (idle) edge [bend left=20] node {} (writelevel) - edge [bend left=20] node {REF} (refresh) - edge node {} (activation) - edge [bend left=10] node {ZQCL/S} (zqcal) - (activation) edge node {} (bankactive) - (bankactive) edge [bend left=30] node {PRE} (prechrg) - edge [bend left=20] node {write} (writeop) - edge [bend right=20] node {read} (readop) - (writeop) edge [loop left] node {write} (writeop) - edge [bend left=10] node {read\_a} (readop) - edge [bend right=15] node {PRE} (prechrg) - (readop) edge [loop right] node {read} (readop) - edge [bend left=10] node {write\_a} (writeop) - edge [bend right=15] node {PRE} (prechrg); - % Thick transitions - \path[->, line width=0.5mm, >=stealth] - (any) edge node {} (reset) - (init) edge node {ZQCL} (zqcal) - (zqcal) edge [bend left=10] node {} (idle) - (writelevel) edge [bend left=20] node {MRS} (idle) - (refresh) edge [bend left=20] node {} (idle) - (writeop) edge node {} (prechrg) - edge [bend left=20] node {} (bankactive) - (readop) edge [bend left=15] node {} (prechrg) - edge [bend right=20] node {} (bankactive) - (prechrg) edge [bend right=20] node {} (idle); - \end{tikzpicture} - \caption{DDR3 controller state machine} - \label{fig:ddr3_state_machine} - \end{figure} + \begin{figure}[H] + \centering + \begin{tikzpicture}[scale=0.6, + transform shape, + shorten >=1pt, + node distance=5cm and 5cm, + on grid, + auto] + % States + \node[state, initial] (reset) {RESET}; + \node[draw=none,fill=none] (any) [below=2cm of reset] {ANY}; + \node[state] (init) [right=of reset] {INIT}; + \node[state] (zqcal) [below=of init] {ZQ Calibration}; + \node[state, accepting] (idle) [right=of init] {IDLE}; + \node[state] (writelevel) [above=of idle] {WRITE LEVELING}; + \node[state] (refresh) [right=of idle] {REFRESH}; + \node[state] (activation) [below=of idle] {ACTIVATION}; + \node[state] (bankactive) [below=of activation] {BANK ACTIVE}; + \node[state] (readop) [below right=of bankactive] {READ OP}; + \node[state] (writeop) [below left=of bankactive] {WRITE OP}; + \node[state] (prechrg) [below right=of readop] {PRE-CHARGING}; + % Transitions + \path[->, line width=0.2mm, >=stealth] + (reset) edge node {} (init) + (idle) edge [bend left=20] node {} (writelevel) + edge [bend left=20] node {REF} (refresh) + edge node {} (activation) + edge [bend left=10] node {ZQCL/S} (zqcal) + (activation) edge node {} (bankactive) + (bankactive) edge [bend left=30] node {PRE} (prechrg) + edge [bend left=20] node {write} (writeop) + edge [bend right=20] node {read} (readop) + (writeop) edge [loop left] node {write} (writeop) + edge [bend left=10] node {read\_a} (readop) + edge [bend right=15] node {PRE} (prechrg) + (readop) edge [loop right] node {read} (readop) + edge [bend left=10] node {write\_a} (writeop) + edge [bend right=15] node {PRE} (prechrg); + % Thick transitions + \path[->, line width=0.5mm, >=stealth] + (any) edge node {} (reset) + (init) edge node {ZQCL} (zqcal) + (zqcal) edge [bend left=10] node {} (idle) + (writelevel) edge [bend left=20] node {MRS} (idle) + (refresh) edge [bend left=20] node {} (idle) + (writeop) edge node {} (prechrg) + edge [bend left=20] node {} (bankactive) + (readop) edge [bend left=15] node {} (prechrg) + edge [bend right=20] node {} (bankactive) + (prechrg) edge [bend right=20] node {} (idle); + \end{tikzpicture} + \caption{DDR3 controller state machine} + \label{fig:ddr3_state_machine} + \end{figure} - ZQ calibration is another vital procedure that adjusts the - output driver impedance and on-die termination (ODT) to match - the system’s characteristic impedance \cite{micron_ddr3}. This - calibration is critical for maintaining signal integrity under - different operating conditions, such as voltage and temperature - changes. During initialization, the memory controller issues a - ZQCL command to the DRAM modules, triggering the calibration - sequence that optimizes impedance settings. - This ensures that the memory system can - operate with tight timing tolerances, which is crucial for - systems requiring high reliability. - Read training is also essential to ensure that data read from - the memory modules is interpreted correctly by the memory - controller. This process involves adjusting the timing of the - read data strobe (DQS) to align perfectly with the data being - received. Proper read training is necessary for reliable data - retrieval, which directly impacts system performance and stability. \\ + ZQ calibration is another vital procedure that adjusts the + output driver impedance and on-die termination (ODT) to match + the system’s characteristic impedance \cite{micron_ddr3}. This + calibration is critical for maintaining signal integrity under + different operating conditions, such as voltage and temperature + changes. During initialization, the memory controller issues a + ZQCL command to the DRAM modules, triggering the calibration + sequence that optimizes impedance settings. + This ensures that the memory system can + operate with tight timing tolerances, which is crucial for + systems requiring high reliability. + Read training is also essential to ensure that data read from + the memory modules is interpreted correctly by the memory + controller. This process involves adjusting the timing of the + read data strobe (DQS) to align perfectly with the data being + received. Proper read training is necessary for reliable data + retrieval, which directly impacts system performance and stability. \\ - ZQCS (ZQ Calibration Short) however is a procedure used - to periodically adjust the DRAM's ODT and output driver impedance - during normal operation. Unlike the full ZQCL (ZQ Calibration Long), - which is performed during initial memory initialization, ZQCS is a - quicker, less comprehensive calibration that fine-tunes the - impedance settings in response to changes in temperature, voltage, - or other environmental factors. This helps maintain optimal signal - integrity and performance throughout the memory's operation without - the need for a full recalibration. \\ + ZQCS (ZQ Calibration Short) however is a procedure used + to periodically adjust the DRAM's ODT and output driver impedance + during normal operation. Unlike the full ZQCL (ZQ Calibration Long), + which is performed during initial memory initialization, ZQCS is a + quicker, less comprehensive calibration that fine-tunes the + impedance settings in response to changes in temperature, voltage, + or other environmental factors. This helps maintain optimal signal + integrity and performance throughout the memory's operation without + the need for a full recalibration. \\ - In summary, the DDR3 memory initialization process in systems - like the ASUS KGPE-D16 involves a series of detailed and - interdependent steps that are critical for ensuring system - stability and performance. These include the detection and - identification of memory modules, the initial configuration of - the memory controller, precise adjustments of timing and voltage - settings, and rigorous training and calibration procedures. + In summary, the DDR3 memory initialization process in systems + like the ASUS KGPE-D16 involves a series of detailed and + interdependent steps that are critical for ensuring system + stability and performance. These include the detection and + identification of memory modules, the initial configuration of + the memory controller, precise adjustments of timing and voltage + settings, and rigorous training and calibration procedures. \section{Memory initialization techniques} @@ -3468,7 +3468,7 @@ uint8_t AddrCmdPrelaunch = 0; /* TODO: Fetch the correct value from RC2[0] */ * 0x41 and 0x0 are the "stock" values */ \end{minted} \end{adjustwidth} - \caption{\texttt{FIXME} indicating the need for + \caption{Lack of mainboard-specific seed overrides, extract from \protect\path{procConfig} function in @@ -3562,7 +3562,7 @@ if (faulty_value_detected) { code. The overcomplicated logic can also make the code more difficult to maintain and extend. \\ - \subsection{DQS position training} + \subsubsection{DQS position training} While the DQS position training algorithm implemented in the \path{TrainDQSRdWrPos_D_Fam15} function may work in some @@ -3714,183 +3714,181 @@ if (best_count > 2) { the time required for DQS position training without compromising accuracy. \\ - \subsection{On a wider scale...} + \subsubsection{On saving training values in NVRAM} - \subsubsection{Saving training values in NVRAM} + The function \path{mctAutoInitMCT_D} is responsible for + automatically initializing the memory controller training (MCT) + process, which involves configuring various memory parameters + and performing training routines to ensure stable and efficient + memory operation. However, the fact that + \path{mctAutoInitMCT\_D} does not allow for the restoration of + training data from NVRAM (lst. \ref{lst:mctAutoInitMCT_D_fixme}) + poses several significant problems. \\ - The function \path{mctAutoInitMCT_D} is responsible for - automatically initializing the memory controller training (MCT) - process, which involves configuring various memory parameters - and performing training routines to ensure stable and efficient - memory operation. However, the fact that - \path{mctAutoInitMCT\_D} does not allow for the restoration of - training data from NVRAM (lst. \ref{lst:mctAutoInitMCT_D_fixme}) - poses several significant problems. \\ + Memory training is a time-consuming process that involves + multiple iterations of read/write operations, delay adjustments, + and calibration steps. By not restoring previously saved + training data from NVRAM, the system is forced to re-run the + full training sequence every time it boots up. This leads to + longer boot times, which can be particularly problematic in + environments where quick system restarts are critical, such + as in servers or embedded systems. \\ - Memory training is a time-consuming process that involves - multiple iterations of read/write operations, delay adjustments, - and calibration steps. By not restoring previously saved - training data from NVRAM, the system is forced to re-run the - full training sequence every time it boots up. This leads to - longer boot times, which can be particularly problematic in - environments where quick system restarts are critical, such - as in servers or embedded systems. \\ + Each time memory training is performed, it puts additional + stress on the memory modules and the memory controller. + Repeatedly executing the training process at every boot can + contribute to the wear and tear of hardware components, + potentially reducing their lifespan. This issue is especially + concerning in systems that frequently power cycle or reboot. \\ - Each time memory training is performed, it puts additional - stress on the memory modules and the memory controller. - Repeatedly executing the training process at every boot can - contribute to the wear and tear of hardware components, - potentially reducing their lifespan. This issue is especially - concerning in systems that frequently power cycle or reboot. \\ + Memory training is sensitive to various factors, such as + temperature, voltage, and load conditions. As a result, the + training results can vary slightly between different boot + cycles. Without the ability to restore previously validated + training data, there is a risk of inconsistency in memory + performance across reboots. This could lead to instability + or suboptimal memory operation, affecting the overall + performance of the system. \\ - Memory training is sensitive to various factors, such as - temperature, voltage, and load conditions. As a result, the - training results can vary slightly between different boot - cycles. Without the ability to restore previously validated - training data, there is a risk of inconsistency in memory - performance across reboots. This could lead to instability - or suboptimal memory operation, affecting the overall - performance of the system. \\ + If the memory training process fails during boot, the system + may be unable to operate properly or may fail to boot entirely. + By restoring validated training data from NVRAM, the system + can bypass the training process altogether, reducing the risk + of boot failures caused by training issues. Without this + feature, any minor issue that affects training could result + in system downtime. \\ - If the memory training process fails during boot, the system - may be unable to operate properly or may fail to boot entirely. - By restoring validated training data from NVRAM, the system - can bypass the training process altogether, reducing the risk - of boot failures caused by training issues. Without this - feature, any minor issue that affects training could result - in system downtime. \\ + Finally, modern memory controllers often include power-saving + features that are fine-tuned during the training process. By + reusing validated training data from NVRAM, the system can + quickly return to an optimized state with lower power + consumption. + The inability to restore this data forces the system to + operate at a potentially less efficient state until training + is complete, leading to higher power consumption during the + boot process. \\ - Finally, modern memory controllers often include power-saving - features that are fine-tuned during the training process. By - reusing validated training data from NVRAM, the system can - quickly return to an optimized state with lower power - consumption. - The inability to restore this data forces the system to - operate at a potentially less efficient state until training - is complete, leading to higher power consumption during the - boot process. \\ + \subsubsection{A seedless DQS position training algorithm} - \subsubsection{A seedless DQS position training algorithm} + An algorithm to find the best timing for the DQS so that the + memory controller can reliably read data from the memory + could be done without relying on any pre-known starting + values (seeds). This would allow for better reliability and + wider support for different situations. The algorithm + could be describe as follows. \\ - An algorithm to find the best timing for the DQS so that the - memory controller can reliably read data from the memory - could be done without relying on any pre-known starting - values (seeds). This would allow for better reliability and - wider support for different situations. The algorithm - could be describe as follows. \\ + \begin{itemize} + \item Prepare Memory Controller: + The memory controller needs to be in a state where it can + safely adjust the DQS timing without affecting the normal + operation of the system. By blocking the DQS signal locking, + we ensure that the adjustments made during training do not + interfere with the controller’s ability to capture data + until the optimal settings are found. - \begin{itemize} - \item Prepare Memory Controller: - The memory controller needs to be in a state where it can - safely adjust the DQS timing without affecting the normal - operation of the system. By blocking the DQS signal locking, - we ensure that the adjustments made during training do not - interfere with the controller’s ability to capture data - until the optimal settings are found. + \item Initialize Variables: + Set up variables to store the various timing settings and + test results for each bytelane. This setup is crucial + because each bytelane might require a different optimal + timing, and keeping track of these values ensures that the + algorithm can correctly determine the best delay settings + later. + \end{itemize} - \item Initialize Variables: - Set up variables to store the various timing settings and - test results for each bytelane. This setup is crucial - because each bytelane might require a different optimal - timing, and keeping track of these values ensures that the - algorithm can correctly determine the best delay settings - later. - \end{itemize} + The main loop is the core of the algorithm, where different + timing settings are systematically explored. By looping + through possible delay settings, the algorithm ensures + that it doesn't miss any potential optimal timings. The + loop structure allows a methodical test of a range of + delays to find the most reliable one. \\ - The main loop is the core of the algorithm, where different - timing settings are systematically explored. By looping - through possible delay settings, the algorithm ensures - that it doesn't miss any potential optimal timings. The - loop structure allows a methodical test of a range of - delays to find the most reliable one. \\ + The gross delay is here the coarse adjustment to the timing + of the DQS signal. It shifts the timing window by a large + amount, helping to broadly align the DQS with the data + lines (DQ). The fine delay, which is the smaller, more + precise change to the timing of the DQS signal once the + coarse alignment (through gross delay) has been achieved, + would then be computed. \\ - The gross delay is here the coarse adjustment to the timing - of the DQS signal. It shifts the timing window by a large - amount, helping to broadly align the DQS with the data - lines (DQ). The fine delay, which is the smaller, more - precise change to the timing of the DQS signal once the - coarse alignment (through gross delay) has been achieved, - would then be computed. \\ + To compute a delay, here would be the steps: - To compute a delay, here would be the steps: + \begin{itemize} + \item Set a delay: + Setting an initial delay allows the algorithm to start + testing. The initial delay might be zero or another default + value, providing a baseline from which to begin the search + for the optimal timing. - \begin{itemize} - \item Set a delay: - Setting an initial delay allows the algorithm to start - testing. The initial delay might be zero or another default - value, providing a baseline from which to begin the search - for the optimal timing. + \item Test it: + After setting the delay, it is essential to test whether the + memory controller can read data correctly. This step is + critical because it indicates whether the current delay + setting is within the acceptable range for reliable data + capture. - \item Test it: - After setting the delay, it is essential to test whether the - memory controller can read data correctly. This step is - critical because it indicates whether the current delay - setting is within the acceptable range for reliable data - capture. + \item Check the result: + If the memory controller successfully reads data, it means + the current delay setting is valid. This information is + crucial because it helps define the range of acceptable + timings. If the test fails, it indicates that the curren + t delay setting is outside the range where the memory + controller can reliably capture data. - \item Check the result: - If the memory controller successfully reads data, it means - the current delay setting is valid. This information is - crucial because it helps define the range of acceptable - timings. If the test fails, it indicates that the curren - t delay setting is outside the range where the memory - controller can reliably capture data. + \item Increase/decrease delay: + By incrementally adjusting the delay, either increasing or + decreasing, the algorithm can explore different timing + settings in a controlled manner. This ensures that the + entire range of possible delays is covered without skipping + over any potential good delays. - \item Increase/decrease delay: - By incrementally adjusting the delay, either increasing or - decreasing, the algorithm can explore different timing - settings in a controlled manner. This ensures that the - entire range of possible delays is covered without skipping - over any potential good delays. + \item Test again: + Re-testing after each adjustment ensures that the exact + point where the DQS timing goes from acceptable (pass) to + unacceptable (fail) is caught. This step helps in + identifying the transition point, which is often the optimal + place to set the DQS delay. - \item Test again: - Re-testing after each adjustment ensures that the exact - point where the DQS timing goes from acceptable (pass) to - unacceptable (fail) is caught. This step helps in - identifying the transition point, which is often the optimal - place to set the DQS delay. + \item Look for a transition: + The transition from pass to fail is where the DQS timing + crosses the boundary of the valid timing window. This + transition is crucial because it marks the end of the + reliable range. The best timing is usually just before + this transition. - \item Look for a transition: - The transition from pass to fail is where the DQS timing - crosses the boundary of the valid timing window. This - transition is crucial because it marks the end of the - reliable range. The best timing is usually just before - this transition. + \item Record the best setting: + Storing the best delay setting for each bytelane ensures + that a reliable timing configuration is available when the + training is complete. - \item Record the best setting: - Storing the best delay setting for each bytelane ensures - that a reliable timing configuration is available when the - training is complete. + \item Confirm all bytelanes: + Before finalizing the settings, it is important to ensure + that the chosen delays work for all bytelanes. This step + serves as a final safeguard against errors, ensuring that + every part of the data bus is correctly aligned. + \end{itemize} - \item Confirm all bytelanes: - Before finalizing the settings, it is important to ensure - that the chosen delays work for all bytelanes. This step - serves as a final safeguard against errors, ensuring that - every part of the data bus is correctly aligned. - \end{itemize} + Each bytelane (8-bit segment of data) may require a + different optimal delay setting. By repeating the process + for all bytelanes, the algorithm ensures that the entire + data bus is correctly timed. Misalignment in even one + bytelane can lead to data errors, making it essential to + tune every bytelane individually. \\ - Each bytelane (8-bit segment of data) may require a - different optimal delay setting. By repeating the process - for all bytelanes, the algorithm ensures that the entire - data bus is correctly timed. Misalignment in even one - bytelane can lead to data errors, making it essential to - tune every bytelane individually. \\ + Once the best settings are confirmed, they need to be + applied to the memory controller for use during normal + operation. This step locks in the most reliable timing + configuration found during the training process. \\ - Once the best settings are confirmed, they need to be - applied to the memory controller for use during normal - operation. This step locks in the most reliable timing - configuration found during the training process. \\ + After the optimal settings are applied, it is necessary + to allow the DQS signal locking mechanism to resume. This + locks in the delay settings, ensuring stable operation going + forward. \\ - After the optimal settings are applied, it is necessary - to allow the DQS signal locking mechanism to resume. This - locks in the delay settings, ensuring stable operation going - forward. \\ - - Finally, the algorithm needs to indicate whether it was - successful in finding reliable timing settings for all - bytelanes. This feedback is crucial for determining whether - the memory system is correctly configured or if further - adjustments or troubleshooting are needed. \\ + Finally, the algorithm needs to indicate whether it was + successful in finding reliable timing settings for all + bytelanes. This feedback is crucial for determining whether + the memory system is correctly configured or if further + adjustments or troubleshooting are needed. \\ % ------------------------------------------------------------------------------ % CHAPTER 5: Virtualization of the operating system through firmware abstraction @@ -4219,7 +4217,8 @@ if (best_count > 2) { \chapter*{Appendix: Long code listings} \addcontentsline{toc}{chapter}{Appendix: Long code listings} -\renewcommand{\thelisting}{\arabic{listing}} +\renewcommand{\thelisting}{L.\arabic{listing}} +\setcounter{listing}{0} \begin{listing} \begin{adjustwidth}{0.5cm}{0.5cm} diff --git a/hardware_init_review.toc b/hardware_init_review.toc index 9784311..4d6d39d 100644 --- a/hardware_init_review.toc +++ b/hardware_init_review.toc @@ -28,28 +28,25 @@ \contentsline {section}{\numberline {3.2}AMD Platform Security Processor and Intel Management Engine}{28}{section.3.2}% \contentsline {chapter}{\numberline {4}Memory initialization and training}{30}{chapter.4}% \contentsline {section}{\numberline {4.1}Importance of DDR3 memory initialization}{30}{section.4.1}% -\contentsline {subsection}{\numberline {4.1.1}General steps for DDR3 configuration}{31}{subsection.4.1.1}% -\contentsline {section}{\numberline {4.2}Memory initialization techniques}{34}{section.4.2}% -\contentsline {subsection}{\numberline {4.2.1}Memory training algorithms}{34}{subsection.4.2.1}% -\contentsline {subsection}{\numberline {4.2.2}BIOS and Kernel Developer Guide (BKDG) recommendations}{35}{subsection.4.2.2}% -\contentsline {subsubsection}{\numberline {4.2.2.1}DDR3 initialization procedure}{36}{subsubsection.4.2.2.1}% -\contentsline {subsubsection}{\numberline {4.2.2.2}ZQ calibration process}{36}{subsubsection.4.2.2.2}% -\contentsline {subsubsection}{\numberline {4.2.2.3}Write leveling process}{37}{subsubsection.4.2.2.3}% -\contentsline {section}{\numberline {4.3}Current implementation and potential improvements}{39}{section.4.3}% -\contentsline {subsection}{\numberline {4.3.1}Current implementation in coreboot on the KGPE-D16}{39}{subsection.4.3.1}% -\contentsline {subsubsection}{\numberline {4.3.1.1}Details on the DQS training function}{41}{subsubsection.4.3.1.1}% -\contentsline {subsubsection}{\numberline {4.3.1.2}Details on the write leveling implementation}{43}{subsubsection.4.3.1.2}% -\contentsline {subsubsection}{\numberline {4.3.1.3}Details on the write leveling implementation}{44}{subsubsection.4.3.1.3}% -\contentsline {subsection}{\numberline {4.3.2}Write Leveling on AMD Fam15h G34 Processors with RDIMMs}{44}{subsection.4.3.2}% -\contentsline {subsubsection}{\numberline {4.3.2.1}Details on the DQS position training function}{45}{subsubsection.4.3.2.1}% -\contentsline {subsubsection}{\numberline {4.3.2.2}Details on the DQS receiver training function}{48}{subsubsection.4.3.2.2}% -\contentsline {subsection}{\numberline {4.3.3}Potential enhancements}{50}{subsection.4.3.3}% -\contentsline {subsubsection}{\numberline {4.3.3.1}DQS receiver training}{50}{subsubsection.4.3.3.1}% -\contentsline {subsubsection}{\numberline {4.3.3.2}Write leveling}{52}{subsubsection.4.3.3.2}% -\contentsline {subsection}{\numberline {4.3.4}DQS position training}{54}{subsection.4.3.4}% -\contentsline {subsection}{\numberline {4.3.5}On a wider scale...}{56}{subsection.4.3.5}% -\contentsline {subsubsection}{\numberline {4.3.5.1}Saving training values in NVRAM}{56}{subsubsection.4.3.5.1}% -\contentsline {subsubsection}{\numberline {4.3.5.2}A seedless DQS position training algorithm}{57}{subsubsection.4.3.5.2}% +\contentsline {section}{\numberline {4.2}General steps for DDR3 configuration}{31}{section.4.2}% +\contentsline {section}{\numberline {4.3}Memory initialization techniques}{34}{section.4.3}% +\contentsline {subsection}{\numberline {4.3.1}Memory training algorithms}{34}{subsection.4.3.1}% +\contentsline {subsection}{\numberline {4.3.2}BIOS and Kernel Developer Guide (BKDG) recommendations}{35}{subsection.4.3.2}% +\contentsline {subsubsection}{\numberline {4.3.2.1}DDR3 initialization procedure}{36}{subsubsection.4.3.2.1}% +\contentsline {subsubsection}{\numberline {4.3.2.2}ZQ calibration process}{36}{subsubsection.4.3.2.2}% +\contentsline {subsubsection}{\numberline {4.3.2.3}Write leveling process}{37}{subsubsection.4.3.2.3}% +\contentsline {section}{\numberline {4.4}Current implementation and potential improvements}{39}{section.4.4}% +\contentsline {subsection}{\numberline {4.4.1}Current implementation in coreboot on the KGPE-D16}{39}{subsection.4.4.1}% +\contentsline {subsubsection}{\numberline {4.4.1.1}Details on the DQS training function}{41}{subsubsection.4.4.1.1}% +\contentsline {subsubsection}{\numberline {4.4.1.2}Details on the write leveling implementation}{43}{subsubsection.4.4.1.2}% +\contentsline {subsubsection}{\numberline {4.4.1.3}Details on the DQS position training function}{45}{subsubsection.4.4.1.3}% +\contentsline {subsubsection}{\numberline {4.4.1.4}Details on the DQS receiver training function}{47}{subsubsection.4.4.1.4}% +\contentsline {subsection}{\numberline {4.4.2}Potential enhancements}{50}{subsection.4.4.2}% +\contentsline {subsubsection}{\numberline {4.4.2.1}DQS receiver training}{50}{subsubsection.4.4.2.1}% +\contentsline {subsubsection}{\numberline {4.4.2.2}Write leveling}{52}{subsubsection.4.4.2.2}% +\contentsline {subsubsection}{\numberline {4.4.2.3}DQS position training}{54}{subsubsection.4.4.2.3}% +\contentsline {subsubsection}{\numberline {4.4.2.4}On saving training values in NVRAM}{56}{subsubsection.4.4.2.4}% +\contentsline {subsubsection}{\numberline {4.4.2.5}A seedless DQS position training algorithm}{57}{subsubsection.4.4.2.5}% \contentsline {chapter}{\numberline {5}Virtualization of the operating system through firmware abstraction}{59}{chapter.5}% \contentsline {section}{\numberline {5.1}ACPI and abstraction of hardware control}{59}{section.5.1}% \contentsline {section}{\numberline {5.2}SMM as a hidden execution layer}{60}{section.5.2}% diff --git a/packages.tex b/packages.tex index e5b2764..47f0fb7 100644 --- a/packages.tex +++ b/packages.tex @@ -33,18 +33,29 @@ \usepackage[a4paper, portrait, margin=1.45cm]{geometry} % Set parameters + +% No warnings \WarningsOff +% No message for text justification +\hbadness=10000 + +% Start at page 0 \setcounter{page}{0} + +% Link every toc element \hypersetup{linktoc=all} +% Enhance footnotes \addtolength{\skip\footins}{0.6pc} \renewcommand*\footnoterule{} %Footnode separator line %\def\siecle#1{\textsc{\romannumeral #1}\textsuperscript{e}~siècle} +% Place dots on sections in toc \renewcommand{\cftsecleader}{\cftdotfill{\cftdotsep}} %places dots on sections lines as well +% Tweak auto-tabulations \cftsetindents{section}{0pt}{4em} \cftsetindents{subsection}{10pt}{4em} \cftsetindents{subsubsection}{20pt}{4em} @@ -52,12 +63,15 @@ \cftsetindents{subparagraph}{40pt}{4em} \def\cftdotsep{1} \cftsetpnumwidth{1em} - -\renewcommand{\cftchapafterpnum}{\vspace{\cftbeforechapskip}} -\renewcommand{\familydefault}{\sfdefault} - \setlength\parindent{0pt} +% Defaults space before chapters +\renewcommand{\cftchapafterpnum}{\vspace{\cftbeforechapskip}} + +% Sans-serif, of course +\renewcommand{\familydefault}{\sfdefault} + +% Configure listings \usemintedstyle{solarized-light} \definecolor{bg}{HTML}{FAF9F6} \definecolor{linenumcolor}{rgb}{0.6, 0.6, 0.6} % Light gray color