程序代写

# ucsbcs154lab6 # All Rights Reserved # Copyright (c) 2023 University of California Santa Barbara # Distribution Prohibited import pyrtl pyrtl.core.set_debug_mode() fetch_pc = pyrtl.Input(bitwidth=32, name=’fetch_pc’) # current pc in fetch update_prediction = pyrtl.Input(bitwidth=1, name=’update_prediction’) # whether to update prediction update_branch_pc = pyrtl.Input(bitwidth=32, name=’update_branch_pc’) # previous pc (in decode/execute) update_branch_taken = pyrtl.Input(bitwidth=1, name=’update_branch_taken’) # whether branch

# ucsbcs154lab6 # All Rights Reserved # Copyright (c) 2023 University of California Santa Barbara # Distribution Prohibited import pyrtl pyrtl.core.set_debug_mode() fetch_pc = pyrtl.Input(bitwidth=32, name=’fetch_pc’) # current pc in fetch update_prediction = pyrtl.Input(bitwidth=1, name=’update_prediction’) # whether to update prediction update_branch_pc = pyrtl.Input(bitwidth=32, name=’update_branch_pc’) # previous pc (in decode/execute) update_branch_taken = pyrtl.Input(bitwidth=1, name=’update_branch_taken’) # whether branch

# ucsbcs154lab6 # All Rights Reserved # Copyright (c) 2023 University of California Santa Barbara # Distribution Prohibited import pyrtl pyrtl.core.set_debug_mode() fetch_pc = pyrtl.Input(bitwidth=32, name=’fetch_pc’) # current pc in fetch update_prediction = pyrtl.Input(bitwidth=1, name=’update_prediction’) # whether to update prediction update_branch_pc = pyrtl.Input(bitwidth=32, name=’update_branch_pc’) # previous pc (in decode/execute) update_branch_taken = pyrtl.Input(bitwidth=1, name=’update_branch_taken’) # whether branch

HW5_Handout PROBLEM #1:¶ The dendritic processing circuit in the figure below is consists of a linear filter with impulse response $h_1(t)$ in cascade with a Biophysical Spike Generator (BSG) which is taken to be an Ideal Integrate-and-fire Neuron; the BSG has a feedback loop that is modeled as a linear filter with impulse response $h_2(t)$.

Project1-Q1-Handout Read the paper, and prove (show your steps) that the model described by equations (2) indeed reduce to the model defined by equations (3) for the initial condition: $x_2(0) = x_6(0)$ and $\dot{x}_2(0) = \dot{x}_6(0)$. Simulate the model defined by the set of equations (3) and (4). Use $\alpha_1=-0.024, \alpha_2=0.0216, \alpha_3=-0.0012, \alpha_4=0.12, C=1.35, \beta=4,

Project2_Handout_2022 Problem 1:¶ Recall the following circuit from HW4: $h^1$ and $h^2$ refer to feedforward filters; $h^{11}$ and $h^{22}$ refer to feedback filters; $H$ refer to summation-cross-feedback filters. Take $BSG_1, BSG_2$ to be IAF neurons with $b=1,\delta=0.01$ and $b=-1,\delta=-0.01$ respectively. Both neurons have $\kappa=1$. Questions:¶ Encode and decode a randomly generated input stimulus using the

HW6_Handout_2022 PROBLEM #1:¶ The dendritic processing circuit in the figure below is consists of a linear spatial filter with impulse response $h_1(x,y)$ in cascade with a Biophysical Spike Generator (BSG) which is taken to be an Ideal Integrate-and-fire Neuron; the BSG has a feedback loop that is modeled as a linear temporal filter with impulse