Adhyayan: Does Groq LPU not use systolic because it uses SRAM for Inference?

"As reading a large static random-ac-cess memory (SRAM) uses much morepower than arithmetic, the matrix unituses “systolic execution” to save energyby reducing reads and writes of the Uni-fied Buffer " ACM paper

Groq’s LPU does use a form of systolic‑like data movement internally, but it’s very different from the typical “systolic arrays” you see in GPUs/TPUs, because of how Groq integrates massive on‑chip SRAM and deterministic scheduling for inference. MEXC+1

Here’s how to think about it clearly:

🧠 1. What “systolic execution” normally refers to

In the context of neural accelerators:

A systolic array (like on a TPU) is a grid of multiply–accumulate units.
Data flows rhythmically between these units, much like blood through a heartbeat‑driven system — hence “systolic.”
This pattern reduces repeated reads/writes from slower memory (like DRAM or HBM) by keeping data moving locally between units.
It relies on memory hierarchies and buffers (e.g., a unified buffer feeding the systolic array) to keep arithmetic units busy with minimal memory traffic.

This matters because reading large memories (e.g., SRAM or DRAM) costs a lot of energy compared to doing arithmetic. Modern GPUs/TPUs optimize arithmetic vs. memory access.
(Your question about SRAM vs arithmetic power cost is exactly this memory bottleneck idea.)

🧠 2. How Groq’s LPU differs

🧩 Massive On‑Chip SRAM Instead of Systolic Buffers

Groq’s LPU:

Stores model weights and activations in hundreds of MB of on‑chip SRAM, not off‑chip HBM or DRAM.
This SRAM is primary storage, not just a cache — and it’s extremely high bandwidth (e.g., ~80 TB/s). Groq+1

This means:

There’s no repeated expensive off‑chip memory fetch to slow down matrix multiply units.
The architecture does not need the traditional unified buffer feeding a separate systolic array like on a TPU or GPU.

So while there are wave‑like data movements internally (the flow of data across compute units), it’s driven by static scheduling and streamlining from the compiler — not a traditional hardware systolic array design. MEXC

This distinction is why folks describe Groq architecture as a “deterministic, compiler‑scheduled data streaming pipeline” rather than a “systolic array plus unified buffer”:

The LPU avoids caches, speculative execution overhead, and dynamic scheduling, and instead pre‑computes every data movement and compute, eliminating idle time and memory stalls. Groq

🧠 3. So does Groq use systolic execution?

Yes — but not in the classic sense.

There is a streaming, rhythmic flow of tensor data through compute units in Groq’s Tensor Streaming Processor (TSP). That is conceptually similar to a systolic pipeline: data moves through an assembly of units in a coordinated fashion. MEXC
However, the way this flow is orchestrated is very different:
- Compiler‑planned steps determine exactly when and where each piece of data goes
- On‑chip SRAM holds all necessary data close to compute units
- This eliminates the need for unified buffers feeding a separate systolic array and avoiding large external memory reads

So rather than having:


[Unified Buffer] → feed → [Systolic Array]

Groq does:


[On‑Chip SRAM] → Static Streaming & Scheduling → [Compute Units]

That streaming looks like systolic waves — but because it’s entirely synchronized and pre‑planned (and avoids slower off‑chip memory), it’s not the same as the classical systolic accelerator design used in GPUs/TPUs.

🧠 4. Why this matters for power and inference

Because SRAM on the chip:

has far lower latency and much higher bandwidth than off‑chip memory, and
reduces power by avoiding repeated reads/writes from slower memory sources. Groq

And because every operation and data transfer is statically scheduled:

there’s no waste from cache misses or runtimes arbitration,
so almost all cycles go directly to useful computation. Groq

This is a different way to reduce power and latency compared to the typical method of using a unified buffer feeding a systolic array — which is great when arithmetic units are starving for data from slower memory.

✅ Summary

Feature	Traditional Systolic Execution	Groq LPU
Memory	External HBM/DRAM + unified buffer	Large on‑chip SRAM
Data Movement	Hardware systolic array driven by buffers	Statically scheduled streaming from SRAM
Scheduling	Dynamic	Fully static (compiler‑planned)
Power Optimization	Reduce off‑chip memory accesses	Eliminate them almost entirely
Arithmetic vs Memory Bottleneck	Systolic array reduces memory fetches	SRAM and static planning obviate the need

So:

Groq’s LPU does use streaming dataflow reminiscent of systolic patterns internally,
but it is not a traditional systolic array fed from a unified buffer,
and the key reason — using large SRAM — means it avoids the energy cost problems that motivate systolic arrays in the first place. Groq

one of the key reasons Groq’s LPU can sustain high utilization, low latency, and low energy use during inference. Let’s unpack how Groq’s compiler schedules executions cycle‑by‑cycle and why that eliminates many of the inefficiencies seen in traditional GPU/accelerator designs.

🧠 1. Static scheduling vs dynamic scheduling

Unlike GPUs (and many other processors), where execution order, memory access, and resource arbitration happen at runtime (leading to unpredictability and stalls), Groq’s LPU uses static scheduling:

The Groq compiler analyzes the entire model and its computation graph before execution.
It determines exact timing and placement for every operation (arithmetic, memory movement, and communication) down to the clock cycle.
There is no runtime arbitration, speculative execution, or hardware cache coherence logic — everything is pre‑planned. Groq+1

This is why Groq often describes the LPU as having deterministic execution:

If the compiler says a workload will take 28.5 ms, then — because every cycle is accounted for — it will take exactly 28.5 ms. Medium

🧠 2. How the compiler schedules everything

Here’s what the compiler does that enables this level of control:

🟦 Latency knowledge

The compiler knows exactly how long each instruction takes and where data must travel on the chip (thanks to the TSP microarchitecture).
With this complete timing info, it can place operations in time and space so that functional units never sit idle waiting for operands. Coding Confessions

🟦 Instruction and data flow scheduling

The compiler effectively builds an assembly‑line schedule of compute and data movement:
- Instruction A emits data at cycle N
- Consumer Unit must consume it at cycle N+X
- The schedule ensures the data arrives just in time
This is unlike dynamic scheduling, where hardware decides on the fly, often reacting to stalls and cache misses. Coding Confessions

🟦 Load‑balanced data movement

Because the compiler controls both where data is stored (on SRAM) and the exact timing of usage, it can proactively move data so it’s waiting for the compute unit right when needed.
It avoids congestion or packet contention that would normally occur on hardware‑managed buses or caches. Coding Confessions

🧠 3. “Assembly‑line” execution model

You can think of the execution model as a programmable conveyor belt:

Data, instructions, and intermediate results are streamed through the chip’s functional units like parts on a conveyor belt in a factory.
Each functional unit knows exactly which cycle it needs the data and what to do with it. There is no waiting for a request to be serviced or for a cache line to arrive.
No speculative execution or cache coherence overhead in hardware means more cycles are spent on actual useful work. Groq+1

This is similar to a hardware‑software co‑designed pipeline where the compiler replaces much of the dynamic control logic normally embedded in the processor.

🧠 4. Synchronization and parallelism

Because the schedule is predetermined:

Groq can overlap different types of parallelism — e.g., tensor parallelism (splitting operations across pieces of hardware) and pipeline parallelism (starting next layer while previous layers still running) — without resource contention.
This would be much harder with runtime scheduling, where synchronization and resource arbitration add delay and unpredictability. Groq

🧠 5. Real deterministic behavior

The practical result is:

Zero tail latency variation — two identical inference requests finish in the exact same number of cycles every time.
Predictable performance — critical in real‑time systems like voice assistants, interactive agents, or SLA‑bound services.
Near‑100% utilization — compute units are never idle because the schedule ensures data arrives just in time. Medium

In contrast, GPU and CPU architectures rely on dynamic scheduling with factors like branch prediction, caches, and speculative execution, which make resource utilization less predictable and introduce cycles where units wait on memory or data transfers.

🧠 Putting it all together

Aspect	Traditional GPU/Accelerator	Groq LPU
Scheduling	Dynamic (hardware manages queues, caches, etc.)	Static (compiler plans every cycle)
Memory access	High latency, unpredictable	On‑chip SRAM, deterministic
Data movement	Reactive (may stall)	Pre‑planned and streamed
Execution predictability	Variable (jitter)	Fixed (cycle‑accurate)
Parallelism	Managed at runtime	Coordinated by compiler
Compute utilization	Variable	High and consistent

🧠 Bottom Line

Groq’s compiler doesn’t just map computations to hardware — it maps every data movement and timing dependency ahead of time. That’s what enables:

cycle‑exact schedules
fully utilized compute units
no runtime stalls or resource contention
deterministic performance every time you run a model

And it’s all powered by the fact that the compiler has complete knowledge of the execution graph, data paths, instruction latencies, and on‑chip SRAM layout before the first cycle ever ticks. Groq+1

Adhyayan

Sunday, December 28, 2025

Does Groq LPU not use systolic because it uses SRAM for Inference?