mirror of https://github.com/VLSIDA/OpenRAM.git
Fixed various issues with imported code from CACTI, added CACTI as an option for analytical sim, added placeholder names in tech files for CACTI
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@ -428,8 +428,11 @@ class spice():
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c_intrinsic = self.get_intrinsic_capacitance()
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# Calculate tau with provided output load then calc delay
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tf = rd*(c_intrinsic+c_load)
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this_delay = horowitz(inrisetime, tf, 0.5, 0.5, True)
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inrisetime = this_delay / (1.0 - 0.5);
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# FIXME: horowitz disabled until other parameters have been
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# fixed due to divide by zero issues
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#this_delay = self.horowitz(inrisetime, tf, 0.5, 0.5, True)
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this_delay = 0
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inrisetime = this_delay / (1.0 - 0.5)
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return delay_data(this_delay, inrisetime)
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def analytical_delay(self, corner, slew, load=0.0):
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@ -495,50 +498,39 @@ class spice():
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self.cell_name))
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return 0
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def horowitz(inputramptime, # input rise time
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def horowitz(self,
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inputramptime, # input rise time
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tf, # time constant of gate
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vs1, # threshold voltage
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vs2, # threshold voltage
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rise): # whether input rises or fall
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{
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if inputramptime == 0 and vs1 == vs2:
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return tf * (-math.log(vs1) if vs1 < 1 else math.log(vs1))
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a = inputramptime / tf
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if rise == RISE:
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b = 0.5;
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td = tf * sqrt(math.log(vs1)*math.log(vs1) + 2*a*b*(1.0 - vs1)) + tf*(math.log(vs1) - math.log(vs2))
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if rise == True:
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b = 0.5
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td = tf * math.sqrt(math.log(vs1)*math.log(vs1) + 2*a*b*(1.0 - vs1)) + tf*(math.log(vs1) - math.log(vs2))
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else:
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b = 0.4;
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td = tf * sqrt(math.log(1.0 - vs1)*math.log(1.0 - vs1) + 2*a*b*(vs1)) + tf*(math.log(1.0 - vs1) - math.log(1.0 - vs2))
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b = 0.4
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td = tf * math.sqrt(math.log(1.0 - vs1)*math.log(1.0 - vs1) + 2*a*b*(vs1)) + tf*(math.log(1.0 - vs1) - math.log(1.0 - vs2))
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return td
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def tr_r_on(width, is_nchannel, stack, _is_cell):
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# FIXME: temp code until parameters have been determined
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if _is_cell:
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dt = tech.sram_cell #SRAM cell access transistor
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else:
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dt = tech.peri_global
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restrans = dt.R_nch_on if is_nchannel else dt.R_pch_on
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def tr_r_on(self, width, is_nchannel, stack, _is_cell):
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restrans = tech.spice["r_nch_on"] if is_nchannel else tech.spice["r_pch_on"]
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return stack * restrans / width
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def gate_c(width, wirelength, _is_cell)
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def gate_c(self, width, wirelength, _is_cell):
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if _is_cell:
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dt = tech.sram_cell #SRAM cell access transistor
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return (tech.spice["c_g_ideal"] + tech.spice["c_overlap"] + 3*tech.spice["c_fringe"])*width +\
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tech.spice["l_phy"]*tech.spice["cpolywire"]
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else:
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dt = tech.peri_global
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return (dt.C_g_ideal + dt.C_overlap + 3*dt.C_fringe)*width + dt.l_phy*Cpolywire
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def drain_c_(width,
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def drain_c_(self,
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width,
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nchannel,
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stack,
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next_arg_thresh_folding_width_or_height_cell,
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@ -551,11 +543,11 @@ class spice():
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if _is_cell:
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dt = tech.sram_cell # SRAM cell access transistor
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else
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else:
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dt = tech.peri_global
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c_junc_area = dt.C_junc;
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c_junc_area = dt.C_junc
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c_junc_sidewall = dt.C_junc_sidewall
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c_fringe = 2*dt.C_fringe
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c_overlap = 2*dt.C_overlap
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@ -583,15 +575,15 @@ class spice():
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num_folded_tr = int(ceil(width / w_folded_tr))
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if num_folded_tr < 2:
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w_folded_tr = width;
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w_folded_tr = width
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total_drain_w = (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) + # only for drain
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(stack - 1) * tech.spacing_poly_to_poly
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# only for drain
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total_drain_w = (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +\
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(stack - 1) * tech.spacing_poly_to_poly
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drain_h_for_sidewall = w_folded_tr
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total_drain_height_for_cap_wrt_gate = w_folded_tr + 2 * w_folded_tr * (stack - 1)
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if num_folded_tr > 1:
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total_drain_w += (num_folded_tr - 2) * (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +
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total_drain_w += (num_folded_tr - 2) * (tech.w_poly_contact + 2 * tech.spacing_poly_to_contact) +\
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(num_folded_tr - 1) * ((stack - 1) * tech.spacing_poly_to_poly)
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if num_folded_tr%2 == 0:
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@ -636,6 +636,8 @@ class lib:
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from .elmore import elmore as model
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elif model_name_lc == "neural_network":
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from .neural_network import neural_network as model
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elif model_name_lc == "cacti":
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from .cacti import cacti as model
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else:
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debug.error("{} model not recognized. See options.py for available models.".format(OPTS.model_name))
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@ -456,6 +456,11 @@ parameter["sa_inv_pmos_size"] = 0.54 # micro-meters
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parameter["sa_inv_nmos_size"] = 0.27 # micro-meters
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parameter["bitcell_drain_cap"] = 0.1 # In Femto-Farad, approximation of drain capacitance
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# Spice Values uses to calculate analytical delay based on CACTI equations
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# FIXME: temp values used currently. Need to be derived from simulations or the SPICE model
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spice["r_nch_on"] = 0
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spice["r_pch_on"] = 0
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###################################################
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# Technology Tool Preferences
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###################################################
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@ -403,6 +403,11 @@ parameter["sa_inv_pmos_size"] = 18 * _lambda_
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parameter["sa_inv_nmos_size"] = 9 * _lambda_
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parameter["bitcell_drain_cap"] = 0.2 # In Femto-Farad, approximation of drain capacitance
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# Spice Values uses to calculate analytical delay based on CACTI equations
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# FIXME: temp values used currently. Need to be derived from simulations or the SPICE model
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spice["r_nch_on"] = 0
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spice["r_pch_on"] = 0
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###################################################
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# Technology Tool Preferences
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###################################################
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