The advent of sub-quadratic AI models, heterogeneous computing, and unified memory architectures represents a pivotal moment in AI system design. As we stand at this technological crossroads, AMD’s evolving unified CPU/GPU architecture, exemplified by the MI300A and its planned successors (MI325, MI350, MI400), combined with their strategic acquisition of Xilinx, offers a compelling case study for re-imagining how AI models can operate.
This exploration examines how the Fidelity framework, with its BAREWire zero-copy technology and F#’s type-safe bit manipulation, is uniquely positioned to leverage AMD’s unified architecture to create a new paradigm for distributed AI inference.
The Ternary Revolution: When Addition Beats Multiplication
Traditional neural networks rely heavily on matrix multiplication, an operation where GPUs excel with their massive parallelism. However, ternary quantization, reducing weights to {-1, 0, +1}, fundamentally changes this equation. By replacing multiplication with simple addition and subtraction, we shift the computational balance dramatically in favor of CPUs and FPGAs.
This shift isn’t merely about performance, it’s about fundamentally rethinking where computation happens. When a CPU can process ternary operations at 512 operations per cycle using AVX-512, while a GPU manages only 2000 ops/cycle, the 4x advantage may not justify the complexity and power consumption of GPU-only deployment. Add Xilinx FPGAs to the mix, with their ability to implement ternary operations directly in configurable logic, and the efficiency gains become even more compelling.
The Art of Bit Packing: 5 Trits in 8 Bits
The mathematical acumen of ternary packing, fitting 5 ternary values into 8 bits (with padding where needed), provides the foundation for efficient storage and computation:
open FSharp.NativeInterop
[<Measure>] type trit
[<Measure>] type packed
let inline byteWithMeasure<[<Measure>] 'u> (b: byte) : byte<'u> =
LanguagePrimitives.ByteWithMeasure<'u> b
let inline intWithMeasure<[<Measure>] 'u> (i: int) : int<'u> =
LanguagePrimitives.Int32WithMeasure<'u> i
type TernaryValue =
| Neg
| Zero
| Pos
| Pad // Padding value for incomplete chunks
member this.ToPackedByte =
match this with
| Zero -> 0uy
| Neg -> 1uy
| Pos -> 2uy
| Pad -> 3uy // Uses base-4 encoding when padding present
static member FromPackedByte (value: byte) =
match value with
| 0uy -> Zero
| 1uy -> Neg
| 2uy -> Pos
| 3uy -> Pad
| _ -> failwith "Invalid ternary value"
// Pack using base-3 for pure ternary or base-4 when padding needed
let packTernary (values: TernaryValue array) : byte<packed> array * int<trit> =
let actualTritCount = intWithMeasure<trit> values.Length
let needsPadding = values.Length % 5 <> 0
if needsPadding then
let paddedValues =
let padding = Array.create (4 - (values.Length % 4)) Pad
Array.append values padding
let packedBytes =
paddedValues
|> Array.chunkBySize 4
|> Array.map (fun chunk ->
let packed =
chunk.[0].ToPackedByte +
chunk.[1].ToPackedByte * 4uy +
chunk.[2].ToPackedByte * 16uy +
chunk.[3].ToPackedByte * 64uy
byteWithMeasure<packed> packed)
(packedBytes, actualTritCount)
else
let packedBytes =
values
|> Array.chunkBySize 5
|> Array.map (fun chunk ->
let packed =
chunk.[0].ToPackedByte +
chunk.[1].ToPackedByte * 3uy +
chunk.[2].ToPackedByte * 9uy +
chunk.[3].ToPackedByte * 27uy +
chunk.[4].ToPackedByte * 81uy
byteWithMeasure<packed> packed)
(packedBytes, actualTritCount)
// Unpack function that handles both base-3 and base-4 encoding
let unpackTernary (packedBytes: byte<packed> array) (actualTritCount: int<trit>) : TernaryValue array =
let isPadded = actualTritCount % (5 * 1<trit>) <> 0<trit>
let allUnpacked =
if isPadded then
packedBytes
|> Array.collect (fun packedByte ->
let b = byte packedByte
[|
TernaryValue.FromPackedByte(b % 4uy)
TernaryValue.FromPackedByte((b / 4uy) % 4uy)
TernaryValue.FromPackedByte((b / 16uy) % 4uy)
TernaryValue.FromPackedByte((b / 64uy) % 4uy)
|])
else
packedBytes
|> Array.collect (fun packedByte ->
let b = byte packedByte
[|
TernaryValue.FromPackedByte(b % 3uy)
TernaryValue.FromPackedByte((b / 3uy) % 3uy)
TernaryValue.FromPackedByte((b / 9uy) % 3uy)
TernaryValue.FromPackedByte((b / 27uy) % 3uy)
TernaryValue.FromPackedByte((b / 81uy) % 3uy)
|])
// Return only actual data (Pad values are always at the end)
allUnpacked.[0 .. (int actualTritCount - 1)]
This 96.9% storage efficiency, combined with SIMD-friendly unpacking operations, enables CPU cores to process ternary operations at speeds approaching specialized hardware, all while maintaining the flexibility to run on commodity processors.
Memory Architecture Evolution: The CXL Advantage
With the convergence on memory unification and AMD’s acquisition of Xilinx, there are now multiple pathways for efficient heterogeneous computing. The CXL (Compute Express Link) protocol becomes particularly crucial here, enabling cache-coherent interconnect between CPUs, GPUs, and now Xilinx FPGAs, each with distinct advantages for ternary model deployment:
MI300A: A Unified Future For AMD
The MI300A APU is the start of AMD’s vision to realize true hardware-coherent shared memory between CPU and GPU:
module UnifiedMemoryInference =
// Single allocation visible to both CPU and GPU
let createUnifiedTensor<'T> (shape: int array) =
let buffer = AMD.allocateUnified<'T>(shape |> Array.reduce (*))
{
Data = buffer
CPUView = buffer.HostPointer
GPUView = buffer.DevicePointer // Same physical memory!
Shape = shape
}
// Zero-copy model distribution
let distributeModel (model: TernaryModel) =
// Attention heads stay on GPU
let attention = createUnifiedTensor model.AttentionShape
// Simple FFN layers on CPU
let ffn = createUnifiedTensor model.FFNShape
// Seamless data flow without copies
{ Attention = attention; FFN = ffn }
Infinity Fabric and CXL: Coherent Interconnect
For discrete GPU systems, Infinity Fabric provides cache-coherent interconnect with promising bandwidth, now enhanced with CXL support for Xilinx FPGA integration:
type InfinityFabricChannel = {
Bandwidth: float<GB/s> // Up to 800 GB/s
Latency: float<ns> // ~120ns
CoherencyProtocol: XGMI
CXLEnabled: bool // For FPGA coherency
}
let setupCoherentChannel (cpu: EPYC) (gpu: MI300X) (fpga: XilinxVersal) =
// Establish coherent link with CXL for FPGA
let fabric = AMD.InfinityFabric.connect cpu gpu
let cxlLink = CXL.establishCoherency fpga
// Allocate in shared coherent memory space
let sharedMemory = CXL.allocateCoherent(size = 16<GB>)
// Map to all processing elements
let mapping = {
CPUAddress = fabric.mapToHost(sharedMemory)
GPUAddress = fabric.mapToDevice(sharedMemory)
FPGAAddress = cxlLink.mapToAccelerator(sharedMemory)
Coherency = CXLCoherencyDomain.Unified
}
mapping
Actor-Based Model Workloads
The true power of heterogeneous ternary inference emerges when we orchestrate multiple specialized models as a group of cooperating actors:
This architecture leverages F#’s actor model to create a flexible, scalable inference system:
// Specialized model actors with hardware affinity
type ModelExpert =
| LanguageExpert of {
Specialization: "translation" | "summarization" | "qa"
Processor: CPUActor
TernaryModel: CompressedBERT
}
| VisionExpert of {
Specialization: "detection" | "segmentation" | "ocr"
Processor: GPUActor
TernaryModel: CompressedYOLO
}
| StreamExpert of {
Specialization: "filtering" | "transformation" | "aggregation"
Processor: FPGAActor // Xilinx Versal
TernaryModel: StreamingNetwork
}
| ReasoningExpert of {
Specialization: "math" | "logic" | "planning"
Processor: HybridActor // CPU + GPU + FPGA
TernaryModel: CompressedCoT
}
// Coordinator with zero-copy message passing
let createConstellation (config: ConstellationConfig) =
let coordinator = MailboxProcessor.Start(fun inbox -> async {
// Pre-allocate shared memory pool with CXL coherency
let memoryPool = BAREWire.createPool {
Size = 64<GB>
AccessMode = CXLUnifiedMemory
Pinned = true
}
// Initialize expert actors including FPGA stream processors
let experts = [
LanguageExpert {
Specialization = "qa"
Processor = CPUActor.spawn 0
TernaryModel = Models.compressedBERT
}
VisionExpert {
Specialization = "detection"
Processor = GPUActor.spawn 0
TernaryModel = Models.compressedYOLO
}
StreamExpert {
Specialization = "filtering"
Processor = FPGAActor.spawn 0
TernaryModel = Models.streamingNetwork
}
ReasoningExpert {
Specialization = "math"
Processor = HybridActor.spawn (cpu = 1, gpu = 0, fpga = 0)
TernaryModel = Models.compressedCoT
}
]
while true do
let! msg = inbox.Receive()
match msg with
| Query(input, replyChannel) ->
// Allocate from shared pool - zero copy
let! sharedBuffer = memoryPool.AllocateAsync(input.Size)
input.CopyTo(sharedBuffer)
// Route to appropriate expert
let expert = selectExpert input.Type experts
let! result = expert.ProcessAsync(sharedBuffer)
replyChannel.Reply(result)
memoryPool.Release(sharedBuffer)
})
coordinator
RDMA and Distributed Scaling
When scaling beyond single nodes, RDMA over Converged Ethernet (RoCE) enables zero-copy operations across the network:
module DistributedConstellation =
// Setup RDMA for inter-node communication
let setupRDMA (nodes: NodeEndpoint array) =
nodes |> Array.map (fun node ->
// Register memory regions for RDMA
let memoryRegion = RDMA.registerMemory {
Buffer = node.ModelMemory
Size = node.ModelSize
Access = IBV_ACCESS_REMOTE_READ ||| IBV_ACCESS_LOCAL_WRITE
}
// Create queue pairs for each connection
let queuePairs = nodes |> Array.map (fun remote ->
if remote.Id <> node.Id then
Some(RDMA.createQueuePair node remote)
else None)
{ Node = node; MemoryRegion = memoryRegion; Connections = queuePairs })
// Zero-copy read from remote node
let readRemoteState (source: NodeConnection) (offset: int<bytes>) (size: int<bytes>) =
// One-sided RDMA read - no CPU involvement on remote side
let request = {
Operation = RDMA_READ
LocalAddress = localBuffer + offset
RemoteAddress = source.MemoryRegion.Address + offset
RemoteKey = source.MemoryRegion.Key
Length = size
}
RDMA.postSend source.QueuePair request
Performance Projections
When just looking at the ‘raw numbers’ the convergence of these technologies could potentially enable remarkable efficiency gains:
| Metric | Traditional GPU-Only | Heterogeneous Ternary | Improvement |
|---|---|---|---|
| Memory Usage | 10GB (FP16) | 500MB (1.58-bit) | 20x reduction |
| Power Consumption | 350W | 95W | 3.7x reduction |
| Latency (1st token) | 45ms | 12ms | 3.8x faster |
| Throughput | 1000 tok/s | 4000 tok/s | 4x increase |
| Cost per Million Tokens | $0.50 | $0.08 | 6.25x cheaper |
While these optimizations are always a balancing act, the improvements could compound when deployed as independent elements:
- Parallel Expert Evaluation: Multiple models process simultaneously
- Intelligent Routing: Only necessary experts activate
- Shared Context: Zero-copy context sharing between models
- Dynamic Scaling: Add/remove experts based on load
- FPGA Stream Processing: Dedicated logic for high-throughput operations
This not only increases efficiency, but also gives unprecedented visibility into and control over how a “solution stack” operates. The “AI” is no longer a black box, but a transparent set of discrete and manageable operators that can be evaluated, adjusted and tuned to suit a specific business outcome.
Implementation Roadmap
The path to making this vision operational involves several key phases:
Phase 1: Foundation
- Implement ternary packing/unpacking kernels for AMD hardware
- Develop BAREWire adapters for Infinity Fabric and CXL coherency
- Create basic actor framework for model coordination
- Deploy initial Xilinx FPGA acceleration kernels
Phase 2: Optimization
- Optimize SIMD kernels for Zen 4/5 architectures
- Implement GPU kernels for residual dense operations
- Configure FPGA dataflow graphs for ternary operations
- Develop profiling tools for workload distribution
Phase 3: Scale
- Add RDMA support for multi-node deployment
- Implement dynamic expert routing algorithms
- Enable CXL memory pooling across heterogeneous accelerators
- Create deployment tools and monitoring
While this path seems revolutionary, the Fidelity framework is uniquely prepared to bring these elements together into a cohesive solution that will provide next-generation efficiency and reliability to intelligent systems.
A New Paradigm Requires Fresh Thinking
The combination of ternary quantization, AMD’s unified memory architecture, and actor-based orchestration represents more than incremental improvement, it’s emblematic of the innovation required to reimagine AI model operation. By embracing the natural sparsity of ternary operations and the flexibility of heterogeneous computing, we can build systems that are not just faster and more efficient, but fundamentally more capable, more manageable and more performant.
AMD’s hardware roadmap is a signal of this potential, particularly the unified memory architecture of MI3x series, the coherent interconnects of Infinity Fabric, and crucially, the Xilinx acquisition that brings FPGA acceleration into the same coherent memory space via CXL. When combined with the Fidelity framework’s type-safe approach and BAREWire’s zero-copy operations, we have uniquely powerful components needed to build the next generation of AI inference systems today while laying the foundation for tomorrow’s hardware breakthroughs.
The future of AI isn’t about ever-larger oceans of matrix multiplication running on ever-more-power-hungry GPUs. It’s about intelligent orchestration of specialized models, each optimized for its task and hardware, working together as a unified system within a business’ security boundary. With ternary quantization breaking the tyranny of matrix multiplication and companies like AMD enabling true heterogeneous computing across CPU, GPU, and FPGA domains, that future is brighter, safer and more efficient than ever.
This exploration builds on SpeakEZ’s ongoing work with the Fidelity framework and BAREWire technology, pushing the boundaries of what’s possible when we rethink fundamental assumptions about AI computation.