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Other register renaming techniques
Tomasulo implements an implicit register renaming: the code is not changed and there is dynamic l...
Speculation and reorder buffer
Hardware-based Speculation Extends the ideas of dynamic scheduling beyond branches combining 3 co...
Caches
The main goal of caches is to increase the performance of a computer through the memory system in...
Improving cache performance
$$\text{AMAT} = \text{Hit Time} + \text{Miss Rate} \times \text{Miss Penalty}$$ In order to impro...
Scoreboard
In the scoreboard architecture we divide the ID stage: Issue: decode instructions, check for st...
Dynamic Branch Prediction
The basic idea of dynamic branch prediction is to use the past branch behavior to predict the fut...
Static Branch Prediction
Static branch prediction is done and fixed at compile time. This technique is typically used when...
Exception handling
The following type of exceptions, interrupts and faults are considered: I/O device request; Invo...
Pipeline hazards
An hazard is created when there is a dependence between instructions and the instructions are clo...
MIPS architecture and pipeline
The MIPS processor is a RISC (Reduced Instruction Set Computer) which follows a LOAD/STORE archit...
Course information
Complete course name: 088949 - ADVANCED COMPUTER ARCHITECTURES (SILVANO CRISTINA) Profesor: Cr...
Dependences
Dependences In an assembly code there may be varius dependences between instructions. The depende...
ARMAX predictors
Starting from an $ARMAX$ process $y(t)=\frac{B(z)}{A(z)}u(t-d)+\frac{C(z)}{A(z)}e(t)$, wehere $e(...
Prediction of non zero mean ARMA
Starting from an $ARMA$ process $y(t)=W(z)e(t)=\frac{C(z)}{A(z)}e(t)$, wehere $e(t) \sim WN(\mu,\...
Linear predictor from output
In the real world we cannot measur the white noise, the only aviable information is the values of...
Long k-step division method
The steady state solution of an $ARMA$ process can be obtained with a long k-step division of $C(...
Linear predictors from noise
Starting from an $ARMA$ process $y(t)=W(z)e(t)=\frac{C(z)}{A(z)}e(t)$, wehere $e(t) \sim WN(0,\la...
Spectrum of ARMA processes
Given the $ARMA$ process $y(t)=\frac{C(z)}{A(z)}e(t)$ with $e(t) \sim WN(0,\lambda^2)$ the spectr...
Frequency domain and spectrum
The frequency domain is another way to obtain the weak (wide sense) characterization of a station...
Auto regressive processes
AR processes Given a zero mean white noise $e(t) \sim WN(0, \lambda^2)$ then the stochastic proce...