diff --git a/extra/cherry.py b/extra/cherry.py
index 500ac7c7..5238cfeb 100755
--- a/extra/cherry.py
+++ b/extra/cherry.py
@@ -179,11 +179,10 @@ binops = {BinaryOps.ADD: riski_add,
 reduceops = {ReduceOps.SUM: riski_reduce_sum,
              ReduceOps.MAX: riski_reduce_max}
 
-#@count
-# TODO: add masks to matmul instruction?
-def riski_matmul():
+@count
+def riski_matmul(slow=False):
   #print("LLL:\n",regfile[Reg.MATMUL_INPUT],"\n",regfile[Reg.MATMUL_WEIGHTS])
-  if False:
+  if not slow:
     regfile[Reg.MATMUL_ACC] += \
       regfile[Reg.MATMUL_INPUT] @ \
       regfile[Reg.MATMUL_WEIGHTS]
@@ -242,12 +241,12 @@ def cherry_dmar(address, arr):
   arr = arr.reshape(-1)
   assert(arr.shape[0] <= SLOTSIZE)
   maxdma = max(maxdma, arr.shape[0])
-  print("DMAR %d elements" % arr.shape[0])
+  #print("DMAR %d elements" % arr.shape[0])
   sram[address:address+arr.shape[0]] = arr
 
 @count
 def cherry_dmaw(address, shp):
-  print("DMAW %d elements" % np.prod(shp))
+  #print("DMAW %d elements" % np.prod(shp))
   return np.copy(sram[address:address+np.prod(shp)].reshape(shp))
 
 # *** CHERRY code to be compiled ***
@@ -294,7 +293,7 @@ def cherry_reduceop(inp, op, axis, keepdims=False):
       osize = nosize
 
   osize = tuple(osize)
-  print("reduce", op, inp.shape, axis, "->", osize, dimlist, redlist)
+  #print("reduce", op, inp.shape, axis, "->", osize, dimlist, redlist)
   inslot, outslot = 0, 2
   cherry_dmar(SLOT(inslot), inp)
 
@@ -303,7 +302,7 @@ def cherry_reduceop(inp, op, axis, keepdims=False):
 
   # special case if redlist ends with True
   if len(redlist) > 0 and redlist[-1] == True:
-    print("special case redlist[-1] == True")
+    #print("special case redlist[-1] == True")
     outside = int(np.prod(dimlist[:-1]))
     for l in range(0, outside, SZ):
       reduce_size = min(SZ, outside-l)
@@ -327,7 +326,7 @@ def cherry_reduceop(inp, op, axis, keepdims=False):
 
   # do the reduce merges one at a time
   while len(dimlist) >= 2:
-    print("proc", dimlist, redlist)
+    #print("proc", dimlist, redlist)
 
     for l in range(0, int(np.prod(dimlist[:-2]))):
       for k in range(0, dimlist[-1], SZ):
@@ -371,7 +370,7 @@ def cherry_binop(x, y, op):
   if not np.all((shape_x == 1) | (shape_y == 1) | (shape_x == shape_y)):
     raise Exception(f"binary op unbroadcastable shape mismatch: {x.shape} vs {y.shape}")
   shape_ret = np.maximum(shape_x, shape_y)
-  print(shape_x, shape_y, shape_ret)
+  #print(shape_x, shape_y, shape_ret)
 
   dimlist, complist = [], [] # note: len(dimlist) may be less than n_dims
   def push(dim, comp):
@@ -382,7 +381,7 @@ def cherry_binop(x, y, op):
   for i in range(n_dims): # group together any adjacent dimensions that we can to simplify broadcasting
     push(max(shape_x[i], shape_y[i]), (shape_x[i] > 1, shape_y[i] > 1))
 
-  print(dimlist, complist)
+  #print(dimlist, complist)
 
   cherry_dmar(SLOT(0), x)
   cherry_dmar(SLOT(1), y)
@@ -518,19 +517,12 @@ class TestCherry(unittest.TestCase):
   def test_mulacc_matmul(self):
     regfile[Reg.MATMUL_INPUT] = np.arange(1, SZ*SZ+1).reshape((SZ, SZ))
     regfile[Reg.MATMUL_WEIGHTS] = np.arange(1, SZ*SZ+1).reshape((SZ, SZ))*-1
-    print(regfile[Reg.MATMUL_INPUT])
-    print(regfile[Reg.MATMUL_WEIGHTS])
     riski_zero(Reg.MATMUL_ACC)
     riski_matmul()
     tst1 = np.copy(regfile[Reg.MATMUL_ACC])
-
-    for l in range(0, SZ):
-      riski_mul(l)
-      print("TST",l,regfile[Reg.MATMUL_OUTPUT])
-      riski_reduce_sum(tgt=l)
+    riski_zero(Reg.MATMUL_ACC)
+    riski_matmul(True)
     tst2 = np.copy(regfile[Reg.MATMUL_ACC])
-    print(tst1)
-    print(tst2) 
     np.testing.assert_allclose(tst1, tst2)
 
 
diff --git a/extra/ops_cherry.py b/extra/ops_cherry.py
index 0dc17c90..ce8c1bac 100644
--- a/extra/ops_cherry.py
+++ b/extra/ops_cherry.py
@@ -176,7 +176,7 @@ class Conv2D(Function):
         #    bs x groups x yx -- groups x 1 --> bs x groups x yx
         #    it looks like a broadcasted multiply
 
-        print("opt1")
+        #print("opt1")
 
         # x:   bs x groups x iy x ix
         # w:        groups x H  x W
@@ -203,7 +203,7 @@ class Conv2D(Function):
                 1, oy*ox, min(SZ, ox-X), min(SZ, groups-g))
 
       elif H == 1 and W == 1 and xs == 1 and ys == 1:
-        print("opt2")
+        #print("opt2")
         # oxy x cin x rcout -- unstrided 1x1
         # this is a simple matmul
         for g in range(0, groups):
@@ -225,7 +225,7 @@ class Conv2D(Function):
                 SLOT(2) + B*groups*rcout*yx + g*rcout*yx + c*yx + YX,
                 1, yx, min(SZ, yx-YX), min(SZ, rcout-c))
       else:
-        print("unoptimized")
+        #print("unoptimized")
         # ox x cin x rcout -- unoptimized
         for g in range(0, groups):
           for c in range(0, rcout, SZ):
diff --git a/fpga/TPUNOTES b/fpga/TPUNOTES
index 8846f760..069ec4a6 100644
--- a/fpga/TPUNOTES
+++ b/fpga/TPUNOTES
@@ -51,10 +51,10 @@ Scalar Unit:
 
 128x Vector Unit:
   32Ki x 32b Vector (Lane) Memory (8 ports)
-  8x:
+  8x: Sublane
      32 x 32b Vector (Lane) Reg File
-     2x ALUs
-     Connections to matrix unix
+     2x ALUs on a float each, 2048 total
+     Connections to matrix unit
 2048 Vector ALUs
 
 2x Matrix Multiply Unit