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diff --git a/openpower/sv/SimpleV_rationale.mdwn b/openpower/sv/SimpleV_rationale.mdwn
index 0d54396358bef0a9980a200572e01a93d612483a..d4e804ec059953dbc947eefdf30571db3cfe2cc1 100644 (file)
--- a/openpower/sv/SimpleV_rationale.mdwn
+++ b/openpower/sv/SimpleV_rationale.mdwn
@@ -11,6 +11,8 @@
 
 # Why in the 2020s would you invent a new Vector ISA
 
+*The short answer: you don't. Leverage and exploit existing technology*
+
 Inventing a new Scalar ISA from scratch is over a decade-long task
 including simulators and compilers: OpenRISC 1200 took 12 years to
 mature.  A Vector or Packed SIMD ISA to reach stable *general-purpose*
@@ -49,15 +51,14 @@ directly integrated into the memory have traditionally not gone well:
 Aspex Microelectronics, Elixent, these are parallel processing companies
 that very few have heard of, because their software stack was so
 specialist that it required heavy investment by customers to utilise.
-D-Matrix and Graphcore are a modern incarnation of the exact same
+D-Matrix, a Systolic Array Processor, is a modern incarnation of the exact same
 "specialist parallel processing" mistake, betting heavily on AI with
 Matrix and Convolution Engines that can do no other task.  Aspex only
 survived by being bought by Ericsson, where its specialised suitability
 for massive wide Baseband FFTs saved it from going under.
 The huge risk is that any "better
 AI mousetrap" created by an innovative competitor
-that comes along will quickly render both D-Matrix and
-Graphcore's approach obsolete.
+that comes along will quickly render the D-Matrix approach obsolete.
 
 NVIDIA and other GPUs have taken a different approach again: massive
 parallelism with more Turing-complete ISAs in each, and dedicated