minor progress..

axon/network.goal

@@ -57,6 +57,41 @@ func (nt *Network) NewState(ctx *Context) {
  // }
 }
 
+// Cycle runs one cycle of activation updating using threading methods.
+func (nt *Network) Cycle() {
+ // todo: chunks of 10 cycles
+ nix := NetIxs()
+ nd := int(nix.NNeurons * nix.MaxData)
+ ld := int(nix.NLayers * nix.MaxData)
+ pd := int(nix.NPools * nix.MaxData)
+ RunGatherSpikes(nd)
+ RunLayerGi(ld)
+ RunBetweenGi(ld)
+ RunPoolGi(pd)
+ RunCycleNeuron(nd)
+ RunSendSpike(nd)
+ RunCyclePost(ld)
+
+ // todo: fix this:
+ // var ldt, vta *Layer
+ // for _, ly := range nt.Layers {
+ // if ly.Type == VTALayer {
+ // vta = ly
+ // } else if ly.Type == LDTLayer {
+ // ldt = ly
+ // } else {
+ // ly.CyclePost(ctx)
+ // }
+ // }
+ // // ordering of these is important
+ // if ldt != nil {
+ // ldt.CyclePost(ctx)
+ // }
+ // if vta != nil {
+ // vta.CyclePost(ctx)
+ // }
+}
+
 //gosl:start
 
 //////// Kernels for all parallel CPU / GPU compute are here:
@@ -152,41 +187,6 @@ func CyclePost(i uint32) { //gosl:kernel
  Layers[li].CyclePost(ctx, di)
 }
 
-// Cycle runs one cycle of activation updating using threading methods.
-func (nt *Network) Cycle() {
- // todo: chunks of 10 cycles
- nix := NetIxs()
- nd := int(nix.NNeurons * nix.MaxData)
- ld := int(nix.NLayers * nix.MaxData)
- pd := int(nix.NPools * nix.MaxData)
- RunGatherSpikes(nd)
- RunLayerGi(ld)
- RunBetweenGi(ld)
- RunPoolGi(pd)
- RunCycleNeuron(nd)
- RunSendSpike(nd)
- RunCyclePost(ld)
-
- // todo: fix this:
- // var ldt, vta *Layer
- // for _, ly := range nt.Layers {
- // if ly.Type == VTALayer {
- // vta = ly
- // } else if ly.Type == LDTLayer {
- // ldt = ly
- // } else {
- // ly.CyclePost(ctx)
- // }
- // }
- // // ordering of these is important
- // if ldt != nil {
- // ldt.CyclePost(ctx)
- // }
- // if vta != nil {
- // vta.CyclePost(ctx)
- // }
-}
-
 //gosl:end
 
 // MinusPhase does updating after end of minus phase

axon/networkbase.goal

@@ -36,6 +36,8 @@ import (
 // network.go has the algorithm-specific code.
 // Everything is defined on Network type.
 
+//gosl:start
+
 // NetworkIndexes are indexes and sizes for processing network.
 type NetworkIndexes struct {
 
@@ -75,6 +77,8 @@ type NetworkIndexes struct {
 
  // total number of .Rubicon Negative USs
  RubiconNNegUSs uint32 `edit:"-"`
+
+ pad uint32
 }
 
 // ItemIndex returns the main item index from an overall index over NItems * MaxData
@@ -113,6 +117,8 @@ func (ix *NetworkIndexes) SynIndexIsValid(si uint32) bool {
  return (si < ix.NSyns)
 }
 
+//gosl:end
+
 // axon.Network implements the Axon spiking model.
 type Network struct {
  emer.NetworkBase

axon/pool.goal

@@ -11,6 +11,7 @@ import (
 )
 
 //gosl:start
+//gosl:import "github.com/emer/axon/v2/fsfffb"
 
 // AvgMaxPhases contains the average and maximum values over a Pool of neurons,
 // at different time scales within a standard ThetaCycle of updating.